4 5. Results Matching of the Training Groups Training Data Age-Related Differences in the Near Transfer of Task-Switching Training to a Similar Switching Task Analysis of Pretest Task-Switching Data Near Transfer of Task-Switching Training: The Influence of Verbal Self-Instructions, Feedback, and Training Variability Far Transfer of Training to Other Executive Tasks and Other Task Domains Structure of Cognitive Abilities Far Transfer to Other Executive Tasks: The Stroop Task Far Transfer to Other Executive Tasks: Verbal Working Memory Far Transfer to Other Executive Tasks: Visuospatial Working Memory Far Transfer to Other Another Task Domain: Fluid Intelligence Control Variables Evaluation of the Training Program Inspection of Training and Transfer Effect Sizes Proportion of Transfer Prediction of Training and Transfer Effects Discussion Summary of Main Results Task-Switching Training: Effects of Age, Verbal Processes, Feedback, and Variability Near Transfer of Task-Switching Training Far Transfer of Task-Switching Training Evaluation of the Transfer Effects and Their Relevance for the Application of Cognitive Training Programs Limitations of the Present Study and Directions for Future Research Conclusion References Appendix Abbreviations Annotation IV

5 List of Tables Table 1: Descriptive Statistics for the Participants Table 2: Schedule of the Present Study Table 3: Overview of Psychometric Measures Table 4: Schedule for the Pretest and Posttest Sessions Table 5: Overview of Stimuli, Task Sets, and Response Assignments Table 6: Means (M) and Standard Deviations (SD) for Speed of Task Execution, General Switch Costs, Perceptual Speed, and Fluid Intelligence as a Function of Age Group and Training Group Table 7: ANOVA Results for the Training Data Based on Log-transformed RT and Accuracy (% Errors) for the Task-Switching Training Groups (Groups 2-5) Table 8: Mean RT (ms) and Standard Deviations (SD) at Pretest as a Function of Age Group and Trial Type; General Switch Costs and Specific Switch Costs as a Function of Age Group Table 9: Summary of the Model-Fitting Procedures Table 10: Stroop Task Mean RT (ms) as a Function of Age Group, Training Group, Session, Trial Type, and Task Version Table 11: Proportion of Participants showing Near and Far Transfer Effects (%) after Single-Task Training and after Task-Switching Training as a Function of Age Group and Transfer Measure Table 12: Regression Analysis Predicting Training Benefits Table 13: Regression Analysis Predicting Transfer Benefits Table 14: Mean RT (ms) and Error Rates (%) as a Function of Age Group and Session for the Single-Task Training Group (Group 1) Table 15: Mean RT (ms) as a Function of Age Group, Training Group, Session, and Trial Type for the Task-Switching Training Groups (Groups 2-5) Table 16: Error Rates (%) as a Function of Age Group, Training Group, Session, and Trial Type for the Task-Switching Training Groups (Groups 2-5) Table 17: Task Switching Mean RT (ms) as a Function of Age Group, Training Group, Session, and Trial Type Table 18: Task Switching Error Rates (%) as a Function of Age Group, Training Group, Session, and Trial Type Table 19: Correlations Between the Psychometric Tests in the Cognitive Battery V

7 List of Figures Figure 1: The two-component model of intellectual development Figure 2: Prototypical findings for age differences in general and specific switch costs Figure 3: General switch costs as a function of verbalization condition and cueing type...54 Figure 4: General switch costs as a function of age group and verbalization condition Figure 5: General Switch Costs (ms) as a function of age group and training...59 Figure 6: Klauer s (2001) strategy for the evaluation of training programs Figure 7: Reduction of general switch costs from pretest to posttest as a function of age group and training group...77 Figure 8: Illustration of the visuospatial working memory tasks used in this study Figure 9: Item from the figural analogies test Figure 10: Task-switching scheme Figure 11: Trial procedure Figure 12: Mean RT (ms) and accuracy (error rates) for the single-task training group as a function of age group and session Figure 13: Mean RT (ms) and accuracy (error rates) for the task-switching training groups as a function of age group, session, and trial type Figure 14: General and specific switch costs at pretest based on mean RT, log-transformed RT, and error rates as a function of age group Figure 15: General switch costs and specific switch costs on the level of mean RT as a function of age group, training group, and session Figure 16: Accepted model M2 with standardized parameter estimates Figure 17: Color Stroop and Number Stroop interference effects (ms) as a function of age group, training group, and session Figure 18: Mean performance for verbal WM (% correct) as a function of age group, training group, and session Figure 19: Mean performance for spatial WM (% correct) as a function of age group, training group, and session Figure 20: Mean performance for fluid intelligence (% correct) as a function of age group, training group, and session VII

8 Figure 21: Figure 22: Figure 23: Figure 24: Figure 25: Effect size (d ) for the training benefits on the level of specific switch costs as a function of age group and training group Effect size (d ) for near transfer benefits on the level of general switch costs and specific switch costs as a function of age group and training group Effect size (d ) for near and far transfer of single-task training and task-switching training as a function of age group and transfer measure Linear curve fits for the prediction of training benefits based on specific switch costs at the beginning of training (session 1) Linear curve fits for the prediction of transfer benefits based on pretest performance for each of the transfer measures VIII

34 Introduction I Theoretical Part 1. Introduction Cognitive training, mental exercising, and brain jogging these and other labels refer to a variety of training concepts supposed to somehow improve one s cognitive performance. The trainings are usually available on the Internet, as a computer program or as a paper-pencil version. Some institutions, such as the adult education center, even offer group trainings. Over the last years, the variety of theoretical concepts and commercial products along with their sales volumes have been constantly increasing. However, considering the large variety of training concepts, one cannot help but wonder: How effective is cognitive training? What exactly makes a given training useful? Which cognitive abilities can be improved? And which individuals benefit most from which type of training? In the clinical context, training programs have become a frequently applied type of intervention in populations with cognitive deficits associated with a wide range of conditions, such as attention-deficit and hyperactivity disorder (ADHD), schizophrenia, head trauma, or dementia. It has been shown that training can lead to significant changes in behavior and brain function in different age groups (for reviews, see Bissig & Lustig, 2007; Jones et al., 2006). Thus, cognitive training provides the opportunity to study age differences in cognitive plasticity, that is, one s ability to improve performance after training. Prior evidence suggests that cognitive plasticity is considerable across the lifespan (e.g., Brehmer, Li, Müller, von Örtzen, & Lindenberger, 2007; Cepeda, Kramer, & Gonzales De Sather, 2001; Derwinger, Stigsdotter Neely, & Persson, 2003; Kramer, Larish, & Strayer, 1995; Kray, Eber, & Karbach, in press; Kray & Lindenberger, 2000; Minear, Shah, & Park, 2002; Schaie & Willis, 1986; Verhaeghen, Marcoen, & Goossens, 1992; for reviews, see Bissig & Lustig, 2007; Jones et al., 26

35 Introduction 2006; Kramer & Willis, 2002), but seems to be limited in very old age (Singer, Lindenberger, & P.B. Baltes, 2003). One aspect receiving more and more attention over the last years is the transferability of training-related benefits to new, unfamiliar situations. This issue seems to be of particular importance for the application of training programs in the clinical and educational context. However, despite the long tradition of training and transfer research, there is still no consensus whether and to which extent transfer can be achieved (for a review, see Barnett & Ceci, 2002). Previous research indicated that the transferability of cognitive training seemed to be limited. Positive transfer was most often confined to near transfer, that is, performance in new, non-trained tasks only improved when these tasks were structurally similar to the training tasks (near transfer), but not if they are structurally dissimilar (far transfer). This suggests that the training primarily tapped task-specific components that could not be transferred to new, structurally dissimilar tasks (e.g. Derwinger et al., 2003; Klauer, 1989a, 1989b; Roth-van der Werf, Resing, & Slenders, 2002). However, a number of recent studies suggest that a larger generalization of training-related benefits can be achieved in different age groups (e.g., Bherer et al., 2005; Dowsett & Livesey, 2000; Klingberg, Forssberg, & Westerberg, 2002b; Klingberg et al., 2005; Kramer et al., 1995; Kramer, Larish, Weber, & Bardell, 1999; Rueda, Rothbart, McCandliss, Saccomanno, & Posner, 2005). Most of the previous work regarding the transfer of training has focused on memory and inductive reasoning (for a review, see Klauer, 2001), but lately a number of studies also has investigated executive functions (e.g., Bherer et al., 2005; Dowsett & Livesey, 2000; Kramer et al. 1995; Kramer, Larish, et al., 1999; Minear, 2004; Minear et al., 2002; Rueda et al., 2005). Executive functions are assumed to be higher-level control processes necessary for the behavioral adaptation to environmental changes and relevant task demands, including action selection and action control. They refer to abilities such as the preparation and maintenance of upcoming tasks, the ability to flexibly switch between them, and the ability to resist to interference (e.g., Duncan, 1995; Kluwe, 1997; Norman & Shallice, 1986; Roberts & 27

36 Introduction Pennington, 1996; Smith & Jonides, 1999). Impairments in different executive control components are associated with a number of neuropsychiatric disorders, including depression, schizophrenia, or attention-deficit and hyperactivity disorder (for a review, see Royall et al., 2002), but similar impairments are also found in children and older adults (e.g., Bedard et al., 2002; Cepeda et al., 2001; Comalli, Wapner, & Werner, 1962; Kray et al., in press; Kray, Eber, & Lindenberger, 2004; Williams, Ponesse, Schachar, Logan, & Tannock, 1999). For instance, it has been shown that the cost of switching between two tasks is smaller in younger adults than in children (e.g., Kray et al., 2004; Crone, Ridderinkhof, Worm, Somsen, & Van Der Molen, 2004) and older adults (e.g., Mayr, 2001; Meiran, Gotler, & Perlman, 2001). Although previous research indicated that the executive control deficits in these age groups can be reduced by training (e.g., Cepeda et al., 2001; Kramer, Hahn, et al., 1999; Kray et al., in press; Kray & Lindenberger, 2000), little is known about the extent to which these training-related benefits can be transferred to new task situations. Therefore, the aim of the present study was to investigate age differences in the near and far transfer of executive control training as well as the modulation of transfer by the type of training. In order to investigate transfer, a pretest - training - posttest design including four session of intensive task-switching training was applied to children (8-9 years of age), younger adults (18 27 years of age) and older adults (62 76 years of age). The influence of the training type was examined by means of five different training conditions. This thesis is divided into a theoretical and an empirical part. The theoretical part first introduces the concept of executive functions and provides an overview of the most important theoretical concepts, the task-switching paradigm applied in this study, and empirical findings regarding lifespan changes in task-switching abilities. Afterwards, theoretical assumptions and empirical evidence with respect to cognitive training are reviewed, followed by a chapter dedicated to the transferability of training benefits across a wide range of ages. Based on this review of the relevant literature, the research hypotheses are developed. The empirical part 28

37 Introduction starts with the introduction of the design and the measures applied in this study, followed by the comprehensive presentation of the results, structured along the research hypotheses, and closes with the extensive discussion of these findings in the light of the relevant literature. 29

38 Executive Control 2. Review of the Literature The review of the literature is divided into three major parts. In the first part, the concept of executive functions is introduced and important theoretical aspects and empirical findings are illustrated, including age differences in executive control. The second part is dedicated to the modulation of these age differences by means of cognitive training. This section includes theoretical assumptions and empirical evidence with respect to the effects of cognitive interventions on age differences in executive control functioning. Finally, the third part presents theoretical and empirical aspects regarding the transferability of these traininginduced improvements to new, unfamiliar task-situations. In addition, it is discussed how transfer can be promoted in different age groups. The theoretical part closes with an extensive summary of the findings most important for the present study and the presentation of the research predictions arising from these previous findings. Executive Control The first chapter of the theoretical part, focusing on executive functions, is subdivided into two sections. The first one introduces important theoretical models and frameworks, while the second one addresses the measurement of executive control, including the task-switching paradigm applied in the present study, and reviews empirical evidence for lifespan changes in executive control with emphasis on the task-switching literature. 30

39 Executive Control Theoretical Concepts of Executive Functions Definition of Executive Functions In everyday live, we often are in situations requiring the selection of one action out of many possible action alternatives. Just imagine, you are driving a car while talking to a fellow passenger and keeping an eye on the navigation system. In scenarios like these, interference needs to be controlled and goal-directed actions have to be maintained, coordinated, and selected appropriately in order to prevent an accident. The cognitive processes responsible for controlling these functions are referred to as executive control functions. Even though a lot of research has focused on executive functions, a generally accepted definition does not exist. However, most investigators agree when it comes to the main characteristics of executive functions: They refer to higher-level processes organizing lower-level processes in order to regulate behavioral activity. Thus, executive functioning is effective when it permits individuals to optimally adapt to continuous changes in the environment (see Baddeley, 1986, 2000; Duncan, 1995; Kluwe, 1997; Logan, 2000; Monsell, 2003; Norman & Shallice, 1986; Roberts & Pennington, 1996; Smith & Jonides, 1999). In line with this concept, Duncan (1995) defined executive control as the ability of the cognitive system to effectively organize its own processing. In addition, he suggested that executive functions are closely linked to intellectual abilities (in the sense of Spearman s g ; Spearman, 1927). This latter aspect will be extensively discussed below (see p. 34). In order to provide a taxonomy of executive functions, a summary of processes associated with this concept has been put forward by Smith and Jonides (1999), including (1) attention and inhibition, (2) task management, (3) planning of task sequences, (4) task monitoring, and (5) coding of working memory representations. According to the authors, attention and inhibition as well as task management appear to be the most elementary and most interrelated processes. However, it is still an open question how executive control is 31

40 Executive Control organized in the cognitive system. Therefore, the most important theoretical views regarding the architecture of executive control are briefly reviewed in the next paragraph. Models of Executive Functions Mostly within the 1980ies and 1990ies, various researchers have proposed theoretical models of executive control. The majority of these traditional models suggested a unitary control system, such as the central executive in Baddeley s (1986, 2000) working memory model or the supervisory attentional system in the model proposed by Norman and Shallice (1986). The latter model, for instance, assumes four components: (1) information processing systems, (2) action and thought schemata, (3) a language system, and (4) a superior control system, the supervisory attentional system (SAS). The schemata are supposed to be fixed sequences for motor and mental actions required for daily life activities, coordinated by lower level contention scheduling processes. On a higher level, the SAS indirectly controls lower level processes by modulating the activation and inhibition of schemata, while coordinating actions and currently relevant task demands at the same time. Thus, the SAS is particularly engaged in activity if confronted with new information, errors, or new action sequences. Therefore, the Norman and Shallice (1986) model accounts for consciously controlled as well as for automated behavior. The fact that control failures, such as utilization behavior 1 (Lhermitte, 1983; for a review, see Archibald, Mateer, & Kerns, 2001) or goal neglect 2 (Duncan, 1993, 1995; Duncan et al., 1996), are typically associated with neurobehavioral disorders in frontal lobe 1 Patients with this disorder are characterized by deficits in inhibitory control, that is, they reach out for objects in the environment and use them in an automatic manner. For instance, the sight of sewing materials will induce sewing, or a plate of food will induce eating. 2 In goal neglect, participants can say exactly what it is they should do, yet show no apparent attempt to do it. [ ] For example, the patient might be asked to watch for a light and to raise one hand when it appears. When the light is switched on, the patient might say I should lift up my hand, yet make no attempt to do so (Duncan, Emslie, Williams, Johnson, & Freer, 1996, p. 131). 32

41 Executive Control patients, has been considered evidence for the assumption of one central executive control mechanism for quite some time. However, despite these findings, the concept of a central control system is seriously doubted these days. Instead, it is widely accepted that executive control is not a unitary construct, but consists of separate control components. Evidence for this view comes from different psychometric studies investigating the organization of executive control in healthy participants (Fisk & Sharp, 2004; Huizinga, Dolan, & Van der Molen, 2006; Miyake et al., 2000). Miyake and colleagues (2000), for instance, used a latent variable approach to examine the unity or diversity of executive functions in a sample of young adults. They included three measures for each of three executive control functions (shifting, updating, and inhibition) in their analysis to examine whether these control functions are indeed separable. Confirmatory factor analysis (CFA) showed that a three-factor model in which the three factors were moderately correlated fitted the data best, supporting both the unity and diversity of executive functioning. In contrast, a three-factor model that did not allow the three latent factors to be correlated (i.e., testing the assumption of three fully independent executive control functions) resulted in a significantly worse fit than the full three-factor model, and so did a one-factor model (i.e., testing the assumption of the unity of executive control). Subsequent studies aimed at the extension of these findings to childhood and older age showed that a twofactor model (updating and shifting) fitted best between the ages of 7 and 21 years (Huizinga et al., 2006), and that a four-factor model (updating, shifting, inhibition, and long-term memory) was most adequate for older adults (up to 81 years of age) (Fisk & Sharp, 2004). All together, these studies argue against the existence of one central executive control mechanism. These results are consistent with data from neuropsychological and neuroimaging research, showing different executive control processes, such as task maintenance, task switching or interference control, to be separable (e.g., Bunge, Dudukovic, Thomason, Vaidya, & Gabrieli, 2002; Crone, Wendelken, Donohue, & Bunge, 2006; Koechlin, Ody, & Kouneiher, 2003; Shallice & Burgess, 1993). Furthermore, experimental data show different components 33

42 Executive Control of executive control to be susceptible to experimental manipulations (e.g., Bedard et al., 2002; Meiran, 2000) as well as individual differences in several aspects of executive control to be highly intercorrelated, but not unitary (Kray & Lindenberger, 2000; Miyake & Shah, 1999). This latter aspect is discussed in detail when age-related differences in task-switching abilities are reviewed below (see p. 45). First, the development of executive control is illustrated within the next section. The Development of Executive Functions and Their Relationship to Intellectual Abilities Over the last years, an increasing amount of research has focused on the development of executive control across the lifespan. This work is particularly important because executive functions are closely related to other cognitive functions, such as intellectual abilities (e.g., Duncan, 1993, 1995). Within this section, the relationship between executive and intellectual abilities is illustrated with reference to their lifespan development. One well-established model with respect to intellectual development is the two-component model of lifespan intellectual development (P. B. Baltes, 1990, 1993). This model is presented in the following section, along with theoretical accounts proposed to explain age differences in intellectual abilities, namely resource-oriented and process-oriented theories. Both are suggesting that one construct of particular importance for the explanation of age differences in intellectual abilities seems to be executive control. The Two-Component Model of Lifespan Intellectual Development. This model proposed by P. B. Baltes (e.g., P. B. Baltes 1990, 1993; see also P. B. Baltes, Lindenberger, & Staudinger, 1998; P. B. Baltes, Staudinger, & Lindenberger, 1999; Lindenberger, 2000) allows the prediction of age-related changes with respect to different domains of intellectual abilities. Based on the model of fluid and crystallized intelligence (Cattell, 1971; Horn, 1970), the authors generally assume two components of intellectual development across the lifespan: 34

43 Executive Control The mechanics and the pragmatics of cognition. The mechanics of cognition refer to basic components of the cognitive system, such as the speed, accuracy, and coordination as well as the classification of elementary processes (cf. P. B. Baltes, 1993). It is assumed that they are influenced by biological factors and invested into the development of the pragmatics of cognition. While the mechanics of cognition strongly increase during childhood and adolescence, they already start declining in middle adulthood, leading to a definite decrement of mechanic abilities in old age (see Figure 1). The pragmatics of cognition, in contrast, refer to knowledge mediated through culturebased socialization across the lifespan, such as verbal knowledge and wisdom-related knowledge. P. B. Baltes (1990) characterized the pragmatics of cognition as context- and knowledge-related applications of the mechanics of cognition. In line with this concept, stability or even growth is found for the development of pragmatics across the lifespan (see Figure 1). Pragmatics (crystallized) Intelligence as culture-based knowledge Mechanics (fluid) Performance Intelligence as basic processes of information processing Lifespan Figure 1: The two-component model of intellectual development. Adapted from P. B. Baltes, Staudinger, & Lindenberger (1999). 35

44 Executive Control Empirical evidence indicates that age-related changes in executive functioning are closely linked to the increase in intellectual functioning during childhood and to its decline in old age (for reviews, see Kray & Lindenberger, 2007; Lindenberger & Kray, 2005). In fact, it has been shown that some measures of executive functions are highly correlated with measures of the mechanics of cognition, such as reasoning or perceptual speed (Kray & Lindenberger, 2000). Consistent with this finding, Duncan (1995) reported the phenomenon of goal neglect in frontal-lobe patients as well as in individuals with lower intelligence (e.g., older compared to younger adults), and Schonfield (1982) claimed that internal attention switches are associated with higher cognitive processes, particularly with fluid intelligence. However, the relationship between intellectual abilities and executive functions becomes more evident when theories with respect to the determinants of age differences in intellectual abilities are introduced in the next section. Determinants of Age Differences in Intellectual Abilities. When it comes to the mechanisms underlying age-related changes in intellectual abilities, two major frameworks have been proposed: Resource-oriented concepts assume a small number of factors underlying age differences across the lifespan, while process-oriented concepts claim that multiple processes are involved. Within the resource oriented account, three ideas have been put forward in order to explain age-related deficits in executive functioning: (1) The slowing of processing speed, (2) reduced inhibitory control, and (3) impaired working memory resources (for reviews, see Kray & Lindenberger, 2007; Lindenberger & Kray, 2005; Lindenberger, 2000). Salthouse s (1996) Processing Speed Hypothesis assumes that a general slowing factor affecting all mental operations causes age differences in various cognitive abilities. According to this model, performance on cognitive tasks (regardless of their type and content) is slowed down by a constant factor in older adults compared to younger adults. Brinley (1965) showed that this 36

45 Executive Control factor typically ranges between 1.5 and 2.0, referred to as general slowing (Verhaeghen & Cerella, 2002). However, more recent studies show that general slowing is not adequate as the sole explanation for age differences in cognitive tasks (e.g., Mayr & Kliegl, 1993; for a meta-analysis, see Verhaeghen & Cerella, 2002). Over the last years, the focus has mainly been on the second and the third approach. Specifically, it is assumed that inhibitory processes play an important role for the explanation of age differences in intellectual abilities. These processes include the ability to control irrelevant information and to focus attention to goal-directed information, that is, key aspects of executive control functioning. Given that inhibitory control increases through childhood and adolescence and declines again in older age (e.g., Comalli et al., 1962; Dempster, 1992; Diamond & Taylor, 1996; Hasher & Zacks, 1988; Lustig, Hasher, & Tonev, 2001), this approach has been used to explain age differences in the performance of various intellectual tasks. Finally, there are theories attributing developmental changes in intellectual abilities to age-related differences in working memory resources (Band, Ridderinkhof, & Segalowitz, 2002; Roberts & Pennington, 1996). Accordingly, a number of studies showed that working memory is a critical factor in accounting for age differences on a broad range of cognitive tasks (e.g., Cherry & Park, 1993; Morell & Park, 1993; Raz, 2000). However, it is certainly possible that no single mechanism will be able to account for all age-related variance on cognitive tasks and that the best estimate of cognitive resource may be a combined measure of sensory function, speed of processing, and working memory (Park & Payer, 2006, p.134). Consistently, structural equation modeling showed that both speed and working memory are important determinants of higher-order cognition (Park et al., 1996; see also Salthouse, 1991). In contrast, process-oriented theories focus on neuroscientific and biological explanations for age differences in intellectual abilities, such as developmental changes in the frontal brain areas, which have also been linked to executive control. Specifically, we know 37

46 Executive Control that the prefrontal cortex (PFC) is of particular importance for executive functions (for reviews, see Casey, Tottenham, Liston, & Durston, 2005; Hedden & Gabrieli, 2004; West, 1996). The PFC develops slower than other brain areas, reaching maturation not until late adolescence, that is, the frontal lobe is among the last brain regions maturing during childhood and adolescence, and also one of the first deteriorating in old age (e.g., Dempster, 1992; Gogtay et al., 2004; Moscovitch & Winocur, 1995; Sowell et al., 2004). Therefore, age-related changes in the frontal lobe have often been linked to age-related differences in executive control abilities, that is, the immature PFC during childhood and a deteriorated frontal lobe in older age are associated with age deficits in executive and intellectual abilities (e.g., Bunge et al., 2002; Dempster, 1992; Diamond & Taylor, 1996; Duncan, 1995; Durston et al., 2002; Goldman-Rakić, 1987; Prull, Gabrieli, & Bunge, 2000; for reviews, see Casey et al., 2005; Hedden & Gabrieli, 2004; West, 1996). Evidence for this view comes from patients with frontal lobe lesions, typically showing performance deficits in a wide range of interference sensitive as well as selective attention tasks, such as the Wisconsin Card Sorting Test and the Stroop Test (e.g., Baddeley, 1996; Diamond & Taylor, 1996; Duncan, 1995; Shallice & Burgess, 1993; Smith & Jonides, 1999). On the neurochemical level, changes in the dopamine metabolism have been linked to age differences in executive and intellectual functioning (e.g., Bäckman et al., 2000; Volkrow et al., 2000); however, these changes also seem to be closely related to PFC development (Raz, 2000). Thus, both resource-oriented and process-oriented accounts suggest a close link between executive control and intellectual abilities, either because one determines the development of the other, or because they rely on similar neuronal structures. It is therefore not surprising that some executive control components and the mechanic component of intellectual abilities seem to have similar developmental trajectories, that is, a marked development from early childhood to adolescence, followed by a continuous decline in older age (e.g., Dempster, 1992; Li et al., 2004; Zelazo, Craik, & Booth, 2004; for a review, see 38

47 Executive Control Craik & Bialystok, 2006). For instance, this u-shaped developmental pattern has been shown for executive control components such as task maintenance and selection (Cepeda et al., 2001; Kray et al., in press; Kray et al., 2004), as well as interference control (Comalli et al., 1962; Dempster, 1992; but see Karbach & Kray, sub.). However, we also know that other components of executive functions, such as the ability to flexibly switch between tasks (e.g., Cepeda et al., 2001; Crone et al., 2004; DeLuca et al., 2003; Karbach & Kray, 2007; Kray et al., in press; Kray et al., 2004; Kray & Lindenberger, 2000) or the ability to stop initiated actions (Bedard et al., 2002; Kray, Kipp, & Karbach, sub.; Williams et al., 1999) are less affected by age. This finding, pointing to the multidirectional development of executive control functions, is illustrated in detail on the basis of control processes involved in task switching within the next section (see p. 45). In sum, age differences in intellectual and executive control abilities are most likely based on general (resources) as well as specific (processes) components. Thus, it seems useful to integrate both aspects (cf. Kliegl, Mayr, & Krampe, 1994) and to consider neuroscientific models and theories (Lindenberger, Li, & Bäckman, 2006). Measurement of Executive Functions A variety of paradigms have been used to assess different aspects of executive functions, among them neuropsychological tests, cognitive tasks, and experimental paradigms. Among the frequently used experimental tasks are, for instance, the dual-task paradigm, the psychological refractory paradigm, and the Stroop task. Given that these paradigms are relevant within the context of the present study, they will briefly be introduced. In dual-task studies, subjects are instructed to perform two simple tasks. Within each trial in a dual-task block, one stimulus for each of these tasks is presented and participants have to respond to both stimuli as fast as possible. These dual-task blocks can be compared to single-task blocks, in which only one of the tasks has to be performed. Performance is 39

48 Executive Control typically slower and more error-prone in dual-task blocks than in single-task blocks. The resulting difference in performance is referred to as dual-task cost, representing the ability to maintain and coordinate multiple tasks (e.g., Kramer et al., 1995; Kramer, Larish, et al., 1999). Dual-task processing has also been investigated by means of the psychological refractory period paradigm (PRP). In this paradigm, two simple tasks are also performed on the same trial. However, the stimulus for task 1 is presented first and followed by the stimulus for task 2 after a variable amount of time. The onset between the stimuli of the two tasks, termed stimulus onset asynchrony (SOA), varies between very short and relatively long intervals. It should be noted that participants are usually instructed to first respond to the stimulus for task 1 and afterwards to the one for task 2. A prototypical finding is that performance in task 2 is impaired when the SOA between the two stimuli is decreased, a finding called the PRP effect (e.g., Maquestiaux, Hartley, & Bertsch, 2004; for a review, see Lien & Proctor, 2002). The Stroop task (Stroop, 1935), in contrast, is designed to measure inhibitory control. Participants are instructed to read color words, which are either presented in the congruent font color (e.g., red in red font), or in an incongruent font color (e.g., red in green font). The typical finding is that participants need more time and make more errors when they have to read the incongruent words than when they have to read congruent words (or words that are not semantically related to colors); this effect is referred to as the Stroop interference effect (for an extensive review, see MacLeod, 1991). Another well-established method to investigate executive control is the task-switching paradigm (for a review, see Monsell, 2003). Given that it was extensively applied in the present study, this paradigm is introduced in detail in the following section, along with the switch cost measures serving as indicators for executive control processes, and theoretical models explaining the origin of these task-switching costs. 40

49 Executive Control The Task-Switching Paradigm and the Operationalization of Task-Switching Costs About eighty years ago, Jersild (1927) introduced the experimental paradigm that we today refer to as task-switching paradigm. Jersild s idea was to compare the time needed to complete a sequence of trials in which people did or did not have to switch between different tasks on successive trials. In the early the 1990ies, Jersild s paradigm was rediscovered and has been applied and modified extensively (for a review, see Monsell, 2003). In task-switching experiments, participants usually see successive stimuli on the computer screen, and they are instructed to perform two simple tasks A and B. A typical example for such a two-choice task is to present round and angular stimuli, either being red or green, and to instruct participants to classify the stimuli by shape (task A) and by color (task B). In order to increase executive control demands, stimuli are often ambiguous, that is, they represent features relevant for both tasks (e.g., a red circle). In order to properly operationalize the task switch, a clear definition of task is needed. Using the more specific term task set, Rogers and Monsell (1995) provided the following specification: To form an effective intention to perform a particular task, regardless of which of the range of task-relevant stimuli will occur, is to adopt a task set. Familiar task sets, such as naming, can be called up from memory. Novel ones can be specified by instructions or other form of training (Rogers & Monsell, 1995, p. 207). In experimental designs, task sets are usually induced by instructions according to the tasks at hand (e.g., classifying stimuli by shape or color). A number of task-switching studies have adopted blocked designs, that is, the experiment consisted of two types of blocks: (1) Sections in which only one task has to be performed separately (A or B), referred to as single-task or task-homogenous blocks, and (2) sections requiring participants to switch between both tasks (A and B), referred to as mixedtask or task-heterogeneous blocks. In order to indicate which task had to be performed in the next trial during mixed-task blocks, either an external task cue was given ( cue-based 41

50 Executive Control switching paradigm ; e.g., Karbach & Kray, 2007; Mayr, 2001; Meiran, 1996) or an alternating task sequence, such as AABBAABB, was applied in mixed-task blocks ( alternating-runs paradigm ; e.g., Kray & Lindenberger, 2000; Rogers & Monsell, 1995). Traditionally, switch costs were calculated as the difference in performance between single-task and mixed-task blocks (e.g., Allport, Styles, & Hsieh, 1994; Jersild, 1927; Kluwe, 1997). In contrast, Rogers and Monsell (1995) assessed switch costs as the difference in performance between switch trials (AB, BA) and nonswitch trials (AA, BB) only within mixed-task blocks. This approach has the following advantage: The task demands for nonswitch and switch trials are the same within mixed blocks, so that switch costs are not confounded with working-memory demands (due to the maintenance of the instruction for both tasks A and B in mixed task blocks), thereby allowing a better identification of the cost associated with the task-shift per se. Kray and Lindenberger (2000) complemented the paradigm proposed by Rogers and Monsell (1995) with a nonswitch baseline (i.e., single-task blocks): The goal was to assess executive control components that were specifically related to the switch situation and control components related to the dual-task situation in general (Kray & Lindenberger, 2000, p. 127). This type of paradigm allows the definition of two types of switching costs: (1) General switch costs, calculated as the difference in performance between single-task and mixed-task blocks, thus measuring the ability to maintain two or more task sets and to select the appropriate one, and (2) specific switch costs, calculated as the difference in performance between switch and nonswitch trials within mixed-task blocks, thereby referring to the mere process of switching between the two tasks. Note that other researchers have adopted the same measures of switch costs under different labels 3. 3 The difference in performance between single-task and mixed-task trials has been referred to as general switch costs (e.g., Kray & Lindenberger, 2000; Kray, Li, & Lindenberger, 2002), global switch costs (e.g., Mayr, 2001), setselection costs (e.g., Kray et al., 2004), and mixing costs (Crone, Bunge, Van der Molen, & Ridderinkhof, 2006; Kray et al., in press; Meiran et al., 2001). Accordingly, the difference in performance between switch and nonswitch trials within mixed-task blocks has been labeled specific switch costs (e.g., Kray & Lindenberger, 2000; Kray et al., 2002), local switch costs (e.g., Mayr, 2001) and switch costs (Crone, Bunge, et al., 2006; Kray et al., 2004; Meiran et al., 2001), respectively. 42

52 Executive Control The Origin of the Task-Switching Costs In search of an explanation for the occurrence of task-switching costs, at least two prominent theories have been widely discussed. One of these approaches emphasizes the role of interference, the other one that of preparation processes. Proponents of the first theory assume that whenever a task switch is performed, the previous task set along with the corresponding stimulus-response mapping is still active ( task set inertia ). This proactive interference impairs the execution of the following new task, so that a complete disengagement from the preceding task is not possible until after the next imperative stimulus has arrived (Allport et al., 1994, p. 437). According to Allport, this theory explains the fact that even after long preparation intervals, residual switch costs are found. Along this line, it has recently been proposed that switch costs arise in response to strong item specific stimulusresponse bindings occurring when participants associate a particular stimulus with a response that must be inhibited when this particular stimulus appears in the context of another task (Waszak, Hommel, & Allport, 2003; see also Kray & Eppinger, 2006). Thus, switch costs in this framework are predominantly the result of bottom-up processes rather than the time taken by top-down control processes necessary to reconfigure a task set. In contrast, Rogers and Monsell (1995) proposed two components of task control being active during task switches: Consciously controlled endogenous control processes support the preparation of the relevant and the inhibition of the irrelevant task set. Thus, switch costs include a measurement of this endogenous reconfiguration process. Exogenous processes that cannot be initiated until after the stimulus has been presented explain the residual switch costs found even after long preparation intervals. The exogenous processes are necessary for completing the reconfiguration process. Thus, the endogenous part can explain why reduced switch costs are see when participants are given more to prepare for a switch while the exogenous process explain why even with a longer preparatory interval, residual switch costs are still observed. 44

53 Executive Control However, after extensively testing both theories, Meiran (1996) showed that none of them could completely explain the occurrence of switch costs, but that reconfiguration processes for the preparation of the new task as well as gradually decreasing interference from the preceding task both play important roles (cf. Cepeda et al., 2001). In sum, the aim of the interference as well as the preparation theories (e.g., Allport et al., 1994; Rogers & Monsell, 1995) was to explain switch costs by one type of control mechanism. However, even though the quantification is still controversial, most investigators today agree that more than one factor contributes to the occurrence of switch costs (see Monsell, 2003). Age Differences in Task-Switching Abilities Most task-switching studies have investigated young adults between 20 and 25 years of age, that is, at the peak level of optimal executive functioning. However, given that this study focused on age differences in task-switching abilities, it was most interesting how general and specific switching abilities change across the lifespan. Lifespan Studies. Lifespan studies investigating the development of task-switching abilities are scarce. Nonetheless, their results are very consistent with respect to general switch costs, typically showing a u-shaped developmental function for this type of costs. That is, general switch costs are larger in children and older adults than in younger adults (Cepeda et al., 2001; Kray et al., in press; Kray et al., 2004; Reimers & Maylor, 2005). When it comes to specific switch costs, results are less clear. Most studies found that age differences in this type of costs are small or not existent, at least when age differences in baseline performance are accounted for (for instance, by means of log-transformed RT; details are provided on p. 124, Method) (Kray et al., in press; Kray et al., 2004; Reimers & Maylor, 45

54 Executive Control 2005). Thus, compared to younger adults, children and older adults are characterized by deficits in the ability to maintain and select two task-sets (i.e., general switch costs), but less in switching per se (i.e., specific switch costs). As an example, the typical pattern of age-related differences in general and specific switch costs is illustrated in Figure 2, based on findings from Kray et al. (2004) and Reimers and Maylor (2005). Both graphs show the u-shaped developmental function for general switch costs, but no age differences in specific switch costs. Although lifespan studies are rare, many studies either focusing on childhood development or on cognitive aging have confirmed this differential age-related pattern with respect to general and specific switch costs. The most important findings are reported below. 250 Swicth Costs (in ms) General switch costs Age Group (yrs) Specific switch costs General switch costs Specific switch costs Figure 2: Prototypical findings for age differences in general and specific switch costs. Figures adapted from Kray, Eber, and Lindenberger (2004) (left panel), and from Reimers & Maylor (2005) (right panel). Childhood and Adolescence. Studies focusing on switching abilities in children younger than 5 years of age usually apply modified versions of the task-switching paradigm, such as the day-night task (e.g., Diamond & Taylor, 1996; Gerstadt, Hong, & Diamond, 1994) or the 46

55 Executive Control dimensional change card sort (DCCS; e.g., Kirkham, Cruess, & Diamond, 2003; Zelazo, Frye, & Rapus, 1996). These studies indicated that children s rule switching ability markedly increases between the ages of 3 and 6 (for reviews, see Garon, Bryson, & Smith, 2008; Zelazo, 1999, 2000; Zelazo & Jacques, 1996). However, given that the card sorting tasks applied to preschool children are not computerized and reaction time is not measured, the analysis is mostly restricted to accuracy measures, and in most experiments no measures comparable to switch costs are calculated. There is one study investigating task-switching abilities in 5-year-old and 9-year old children by means of a cue-based switching paradigm (Karbach & Kray, 2007). Results showed substantially larger general switch costs in 5-year-olds than in 9-year-olds, but no age differences in specific switch costs, suggesting that both capabilities reflect separate developmental trajectories rather than a unitary trend. This result joins others in demonstrating that age functions for general and specific switching abilities are not unitary across childhood (Cepeda et al., 2001; Crone, Bunge, et al., 2006; Crone et al., 2004; Kray et al., 2004). Although 5-years-olds are able to represent a higher order rule allowing them to select between two tasks (Zelazo, 1999; Zelazo & Frye, 1998), task-set selection is still associated with substantial costs in this age group. With increasing age, these costs are substantially reduced, that is, children s ability to maintain and select task-sets improves (cf. Diamond & Taylor, 1996; Gerstadt et al., 1994). This view is supported by findings from Chelune and Baer (1986), reporting a linear increase in set maintenance between the ages of 6 and 10 in the Wisconsin Card Sorting Test (WCST; see also DeLuca et al., 2003; Welsh, Pennington, & Groisser, 1991). Consistently, Huizinga and Van der Molen (2007) reported that children s ability for set switching in the WCST reached adults levels at the age of 11, while the ability for set maintenance reached this level at the age of 15. A task-switching study from Crone and colleagues (2004) consistently revealed that the increase in the ability to maintain task sets 47

56 Executive Control was most pronounced between the ages of and years of age (in cued tasks) 4. In sum, these findings point to an earlier maturation of task-set switching than task-set maintenance, possibly associated with maturation of different sub-regions of the PFC (cf. Crone et al., 2004; Stuss, 1992; Van der Molen, 2001; for a review, see Casey et al., 2005). Cognitive Aging. Evidence for adult age differences in task-switching abilities is considerable these days (for a review, see Kramer & Kray, 2006). Kray and Lindenberger (2000), for instance, used an alternating-runs paradigm in order to investigate adult age differences (20 to 80 years) in both general and specific switch costs. They found larger age differences in general switch costs than in specific switch costs (see also Kray, 2006; Kray et al., in press; Kray et al. 2004), and this effect was resistant to practice and increased preparation time. Based on this finding, the authors concluded that age-related deficits particularly concern task-set maintenance and selection, at least in situations where no external task cue is given and hence high demands on executive control are imposed (Kray & Lindenberger, 2000). Mayr (2001) replicated the finding of larger age differences in general compared to specific switch costs with a cued version of the task-switching paradigm, that is, under reduced working-memory demands (but see Kray et al., 2002). Moreover, he found that the amount of interference at the stimulus and response level (stimulus ambiguity and responseset overlap 5 ) modulated age differences in general switch costs. Interestingly, age differences 4 Crone, Bunge, and colleagues (2006), however, reported conflicting results: They found no age differences in general switch costs between younger children (7-8 years), older children (10-12 years), and young adults (20-25 years). However, it should be noted that in their study general switch costs were defined as the difference between single-task trials and nonswitch trials (instead of singe-task blocks vs. mixed-task blocks). 5 Stimulus ambiguity refers to the fact that stimuli represent attributes of both tasks involved in task switching. Participants in the Mayr (2001) study, for instance, were instructed to classify pictures by shape (circle or square?) and by color (red or green?). The ambiguous stimuli featured attributes relevant for both tasks, such as red squares or green circles. Response set overlap refers to the fact that two response alternatives were mapped onto the same response button, that is, one button for the response alternatives circle (for the shape classification) and green (for the color classification), and 48

57 Executive Control in general switch costs disappeared when there was no overlap at the stimulus and response level, suggesting that older adults particularly have problems to separate overlapping task-set representations. Mayr (2001) further assumed that this pattern of age differences may be due to an updating process performed by older participants to internally re-ensure task sets in nonswitch as well as in switch trials within mixed-task blocks. This strategy results in relatively large general and small specific switch costs. Younger adults, in contrast, are supposed to rely on this updating strategy in switch trials only. This interpretation is supported by neuroscientific evidence, revealing neural activation in the dorsal and medial prefrontal cortex while performing nonswitch as well as switch trials in older adults, while younger adults only show this activation in switch trials (DiGirolamo et al., 2001). De Jong (2001), in contrast, applied an alternating runs paradigm and found larger general as well as specific switch costs in older adults compared to younger adults (cf. Bherer et al., 2005; Meiran et al., 2001). This finding is consistent with previous results based on cued switching paradigms, indicating that older adults showed larger specific switch costs than younger adults (Kramer, Hahn, & Gopher, 1999; Salthouse, Fristoe, McGuthry, & Hambrick, 1998). De Jong (2001) found that under time pressure induced by reducing the response time interval, age differences in specific switch costs were unaffected, while those in general switch costs disappeared. Based on this finding, he concluded that age differences in general switch costs are caused by a more conservative response bias in old adults during mixed-task blocks, resulting in larger general switch costs in old adults compared to younger adults in situations without time pressure. However, despite of some conflicting findings (for a review, see Kramer & Kray, 2006), most studies comparing the performance of younger and older adults found substantial age differences in general switch costs beyond general slowing, while specific switch costs were the other response button for the response alternatives square and red. Designs with ambiguous stimuli and response set overlap induce high interference at the stimulus and the response level. 49

58 Executive Control relatively stable across the adult lifespan (for a meta-analysis, see Verhaeghen & Cerella, 2002). These findings point to an earlier decline of task-set maintenance and selection than task-set switching probably associated with the age-related development of different subregions of the PFC assumed to be extensively involved in multitask-processing and task coordination (cf. Braver et al., 2001; for reviews, see Hedden & Gabrieli, 2004; West, 1996). In sum, children and older adults show marked deficits in the ability to maintain and select two task sets, but less in the ability to switch between tasks (e.g., Crone et al., 2004; Karbach & Kray, 2007; Kray, 2006; Kray et al., in press; Kray et al., 2004; Kray & Lindenberger, 2000; Mayr, 2001; Reimers & Maylor, 2005; cf. Verheagehn & Cerella, 2002). The relative amount of age differences in both types of switching costs depends on a number of factors, among them the cueing type (e.g., van Asselen & Ridderinkhof, 2000), preparation time (e.g., De Jong, 2001; Meiran et al., 2001), task ambiguity (Rubin & Meiran, 2005), and the amount of interference (e.g., Mayr, 2001). Given that these manipulations were not the focus of the present study, they are not discussed in more detail. Instead, the next section is dedicated to an aspect highly relevant for the present study, namely the question whether agedifferences in task-switching abilities can be influenced by means of cognitive interventions, including intensive training and the application of verbal strategies. 50

59 Cognitive Training Cognitive Training Cognitive interventions (Kramer & Willis, 2002) provide the opportunity to study age differences in cognitive plasticity, that is, the ability to improve one's performance through instruction and training 6 (cf. Singer & Lindenberger, 2000). The plasticity of cognitive abilities across the lifespan varies within the age-related limits of mechanic abilities (cf. Kray & Lindenberger, 2007). Participants across a wide age range usually show performance improvements after cognitive interventions (for reviews, see Jones et al., 2006; Kramer & Willis, 2002). However, given that the development of fluid intelligence as well as several executive control abilities is characterized by a u-shaped lifespan pattern (see p. 34), the question is whether cognitive plasticity is already present in childhood and maintained in older age. Most evidence for developmental differences in cognitive plasticity comes from the domain of memory. In one study, for instance, participants (age range: 9 78 years of age) learned and practiced an imagery-based mnemonic technique to encode and retrieve words by location cues (Brehmer et al., 2007). While subjects in all age groups were able to optimize the use of the technique, children benefited more from training and reached higher performance levels than older adults. However, other studies indicated that older adults nevertheless show considerable cognitive plasticity, not only in the area of fluid intelligence (e.g., Schaie & Willis, 1986), but also with respect to memory (e.g., Derwinger et al., 2003; Verhaeghen et al., 1992) and executive control (e.g., Cepeda et al., 2001; Kramer, Hahn, et al., 1999; Kramer et al., 1995; Kramer, Larish et al., 1999; Kray et al., in press; Kray & 6 It should be noted that the terms training and practice have been used with different connotations in the literature. Some authors refer to practice as repeatedly performing a certain task, while training is characterized by the additional use of some strategy, such as performing a mnemonic strategy during memory training (e.g., Derwinger et al., 2003). However, in numerous publications the terms are not clearly defined and used interchangeable. Given that the cognitive intervention applied in the present study (for details, see Method) partly qualifies as practice and partly as training intervention, the terms will also be used interchangeable within the context of the present study. 51

60 Cognitive Training Lindenberger, 2000; Minear et al., 2002; see also p. 57). The Seattle Longitudinal Study (Schaie, 1996), for instance, showed that the degree of training-related benefits in fluid intelligence corresponded to the degree of longitudinally assessed decline over the previous 15 to 20 years (Schaie, 1996; Schaie & Willis, 1986). Still, this cognitive plasticity seems to be limited in very old age (Singer et al., 2003) and in patients suffering from dementia, so that the reduction of cognitive plasticity has been used for the early diagnosis of dementia (Bäckman, 1992; M. Baltes, Kühl, Gutzmann, & Sowarka, 1995). This section provides an overview of the two types of cognitive interventions most important for this study, that is, the influence of verbal self-instruction strategies and intensive task training on age differences in taskswitching performance. Verbal Self-Instructions Since Vygotsky s (1962) and Luria s (1960, 1969) work, it is well known that language has a regulatory function in action control, for instance, by focusing attention to the information most relevant for the task at hand. The first studies providing evidence for verbal selfinstruction training date back at least to the 1960ies and 1970ies. Meichenbaum and Goodman (1971), for instance, developed a cognitive self-guidance program mainly based on Vygotsky s (1962, 1988) and Luria s (1960, 1969) developmental theories, describing a gradual internalization of self-directing external speech ( private speech ) as a function of age, thus, a developmental progression from external to internal control of behavior 7. The training program turned out to be effective for improving performance in serial recall tasks (Asarnow & Meichenbaum, 1979) as well as in reasoning tasks among children with inhibitory control deficits (Meichenbaum & Goodman, 1971). Today, verbal self-instruction trainings are well 7 Vygotsky (1988), for instance, suggested that private speech is a developmental stage preceding inner speech, that is, the transition from social activity to more individualized activity, reflecting the child s gradual individualization. He claimed that private speech gradually develops regarding its structural and functional characteristics, leading to a differentiation from external speech. As a consequence, the vocalization fades away, and in the end, it becomes inner speech (Vygotsky, 1988, p.183). 52

61 Cognitive Training established in the field of cognitive-behavioral therapy (for a review, see Gosch, Flannery- Schroeder, Mauro, & Compton, 2006). Aside from self-instruction training for children, verbal strategies have also been proposed as compensation for age-associated cognitive deficits, such as memory deficits, in older age (Meichenbaum, 1974). Derwinger and colleagues (2003), for instance, trained older adults to use a mnemonic strategy in a number recall task. Performance improved reliably following training and the training-related gains even increased when support (i.e., verbal cues) was provided during the recall. This result confirms previous research by showing that systematic training can indeed enhance healthy older adults performance in memory tasks, thus demonstrating the effectiveness of verbal self-instructions and memory plasticity in old age (Stigsdotter Neely, 2000; Verhaeghen, 2000). However, of particular importance for this study was the influence of verbal-self instructions on age differences in executive control functioning in general, and more specifically, on age differences in task-switching performance. Within models of executive control, the role of verbal processes (i.e., inner speech) is limited to the maintenance of information. In Baddeley s (1986, 2000) working memory model, for instance, inner speech is closely linked to the phonological loop system, and more specific, to the articulatory rehearsal processes responsible for maintaining phonological information in working memory. Based on this model, the influence of verbal processes on executive control functioning has recently been investigated in task-switching studies with adult participants (Baddeley, Chincotta, & Adlam, 2001; Bryck & Mayr, 2005; Emerson & Miyake, 2003; Goschke, 2000; Gruber & Goschke, 2004; Miyake, Emerson, Padilla, & Ahn, 2004; Saeki & Saito, 2004). In these studies, participants were instructed to perform a secondary verbal task (for instance, saying aloud an over-learned sequence of words, such as the days of the week) to disrupt the use of inner speech during task preparation. Nearly all of these studies showed that maintaining and selecting task sets (e.g., Emerson & Miyake, 2003; Saeki & Saito, 2004) 53

62 Cognitive Training was more sensitive to articulatory suppression than switching per se (e.g., Bryck & Mayr, 2005), that is, general switch costs were substantially increased under articulatory suppression conditions. Emerson and Miyake (2003) showed that the increase in general switch costs was most pronounced when external task cues were either abstract or not present, suggesting that inner speech is of major importance for the retrieval and activation of the next task goal, at least when the availability of external cues is limited and the need for endogenous control is enhanced (see Figure 3; cf. Baddeley et al., 2001; Miyake et al., 2004; Saeki & Saito, 2004). Taken together, these results support the view that inner speech plays an important role for the preparation of upcoming tasks by effectively facilitating the retrieval of the phonological representation of the next task goal (see also Mecklinger, von Cramon, Springer, & Matthes-von Cramon, 1999). Figure 3: General switch costs as a function of verbalization condition (articulatory suppression, control) and cueing type (color cue, symbol cue, no cue). Figure adapted from Emerson and Miyake (2003). Recently, studies have not only investigated effects of disrupting inner speech by means of task-irrelevant verbalizations (i.e., articulatory suppression), but also effects of supporting it by means of task-relevant verbalizations (i.e., verbal self-instructions). However, developmental studies focusing on age differences in the use of verbal self-instructions for 54

63 Cognitive Training flexibly switching between task sets are scarce (e.g., Karbach & Kray, 2007; Kray et al., in press; Kray et al., 2004). Nevertheless, they have provided evidence indicating that particularly younger children and older adults can use language to compensate for executive control deficits. A recent study, for instance, examined the influence of verbal processes on age differences in task switching in younger (7 9 years of age) and older (11 13 years of age) children, and in younger (20 27 years of age) and older adults (66 77 years of age) (Kray et al., in press). In order to increase the use of inner speech during task preparation, an internally cued switching paradigm without external task cues was applied. The role of verbal processes for task-switching performance was investigated by instructing participants to perform a secondary verbalization task. Three verbalization conditions were compared: Switching performance (1) without verbalization, (2) when subjects named the next task goal during task preparation, and (3) when subjects verbalized irrelevant words (the, the, the [der, die, das]). Compared to the control condition without verbalization, the u-shaped developmental trend for general switch costs was more pronounced when the use of inner speech was disrupted by the verbalization of irrelevant words during task preparation. In contrast, age differences in general switch costs were reduced when subjects named the next task goal during task preparation, especially for younger children and older adults (see Figure 4). Thus, especially younger children and older adults were able to use language for the compensation of age-related deficits in the ability to maintain and select task goals. Evidence for developmental changes in the use of language for executive control functioning in older age comes from the cognitive neurosciences. In a recent study focusing on training-induced plasticity in older age, Erickson et al. (2007b) found that in contrast to younger adults, older adults showed an increase in activation in the left ventrolateral prefrontal cortex close to the Boca s area with increasing dual-task training. The authors suggested that after training, older adults seem to increasingly rely on verbal processes to manage and 55

64 Cognitive Training coordinate multiple task demands, indicating that there are age-related changes in the use of verbal processes for efficient task control. 400 Control (no Verbalization) Task-relevant Verbalization General Switch Costs (ms) Younger Children Older ChildrenYounger Adults Older Adults Younger Older Younger Older Children Children Adults Adults Figure 4: General switch costs (ms) as a function of age group (younger children, older children, younger adults, older adults) and verbalization condition (task-relevant, control). Figure adapted from Kray, Eber, and Karbach (in press). Over the last years, some studies also showed that even very young children can use language for efficient action control. Kirkham and colleagues (2003), for instance, demonstrated that verbal labeling supported the ability to switch to a new set of rules in 3- year-old children, suggesting that speech helps to redirect attention (cf. Towse, Redbond, Houston-Price, & Cook, 2000). In addition, Müller, Zelazo, Hood, Leone, and Rohrer (2004) found that performance in conflict-control tasks in the same age group improved when children were asked to use verbal labeling during testing. Finally, there is also evidence indicating that children s reorientation ability was related to the productive use of spatial language (cf. Hermer-Vazquez, Spelke, & Katsnelson, 1999), and that action-effect learning in 4-year-olds is strongly mediated by the way the actions and their outcomes are verbally described (Kray, 56

65 Cognitive Training Eenshuistra, Kerstner, Weidema, & Hommel, 2006). In sum, these findings point to an important function of language for the cognitive development in different domains and across a wide range of ages. Training Research focusing on training-related benefits in task-switching abilities shows considerable potential for cognitive plasticity, but also its limits. From a theoretical point of view, one may assume that task-switching costs are only a temporary phenomenon, emerging because participants have to execute unfamiliar tasks and because they are not familiar with the process of switching between them. If this was true, intensive training would lead to an automatization of the underlying executive control abilities, resulting in a disappearance of switch costs (Kluwe, 1997; Rogers & Monsell, 1995). Logan (1988) refers to the process of automatization as a memory-based process. When novices are required to perform a task, they use a general algorithm at first. While practicing the task, domain specific knowledge increases. Eventually, this knowledge allows retrieving specific information directly from memory, so that the algorithm can be dropped: Automatization reflects a transition from algorithm-based performance to memory-based performance (Logan, 1988, p. 493). Assuming that the increasing automatization of the single component tasks involved in task switching leads to faster performance after practice, the actual question is whether this speeding is also present on the level of executive control functions (i.e., general and specific switch costs)? Indeed, several studies showed that training can reduce general as well as specific switch costs, but that residual switch costs were found even after extensive practice (e.g., Allport et al., 1994; Cepeda et al., 2001; De Jong, 2001; Jersild, 1927; Kramer, Hahn, et al., 1999; Kray et al., in press; Kray & Lindenberger, 2000; Minear, et al., 2002; Rogers & Monsell, 1995). The practice effects observed in these studies have mostly been attributed to task 57

66 Cognitive Training specific effects, such as the strengthening of stimulus-response rules or specific associations between cues and tasks (Jersild, 1927; Meiran, 1996; Rogers & Monsell, 1995). For the purpose of the present study, it was particularly important whether there are age-related differences in the amount of training-related benefits on the level of task-switching abilities. This question is especially important because it sheds light on the potentials and limits of plasticity in executive control functioning in different age groups. Kramer, Hahn, and colleagues (1999), for instance, examined age differences with respect to training effects by means of a cue-based switching paradigm. Compared to younger adults, older adults showed a larger reduction of specific switch costs as a function of training. After three sessions of training, older adults had reduced their specific switch costs to such an extend that age differences between younger and older adults disappeared. This benefit was still found in a follow-up session two months after the training had ended. In a second experiment, the authors increased the working-memory demands by applying an internally cued paradigm without external task cues, that is, participants had to switch tasks on every fourth trial. Under these conditions, older participants were not able to reduce their specific switch costs. Kray and Lindenberger (2000) investigated both general and specific switch costs across six sessions of training in younger and older adults by means of an internally cued switching paradigm. General as well as specific costs were reduced as a function of practice, (cf. Bherer et al., 2005) but still substantial in the last training session. Although older adults showed a larger reduction of general switch costs than younger adults (cf. Minear et al., 2002), age differences were still reliable. Further evidence for practice effects in older age comes from the cognitive neurosciences: Erickson and colleagues (2007a) investigated the degree of plasticity in regions involved in the management and coordination of multiple task-sets in older adults by means of a dual-task training approach. They found that the training-induced changes in activation occurred in cortical areas often associated with the largest age-related 58

67 Cognitive Training atrophy, that is, the dorsal and ventral prefrontal cortex (Raz, 2000; West, 1996). The authors concluded this suggests that age-related functional decline in these regions is not an inevitable process of aging, but that it can be reliably reduced and possibly reversed with training (Erickson et al., 2007a, p. 9). When it comes to children, evidence for task-switching training is rare. Results from Cepeda and colleagues (2001) indicated that general switch costs were reduced after training, and that this reduction was larger for children (10-12 years of age) and older adults than for younger adults (see also Kray et al., in press; Figure 5). This is consistent with Eber and Kray (in prep.), showing that children between 8 and 10 years of age were able to reduce general (but not specific) switch costs across two sessions of training. 400 Training Session 1 Training Session 3 General Switch Costs (ms) Younger Children Older Children Younger Adults Older Adults Younger Older Younger Older Children Children Adults Adults Figure 5: General switch costs (ms) as a function of age group (younger children, older children, younger adults, older adults) and training (session 1, session 3). Figure adapted from Kray, Eber, and Karbach (in press). In sum, developmental studies investigating training-related benefits in task-switching performance are relatively rare. However, their results indicate that the ability to maintain and 59

68 Cognitive Training select task sets and in some studies also the ability to switch between them can be improved by training, pointing to considerable plasticity regarding executive control functioning (e.g., Cepeda et al., 2001; Eber & Kray, in prep.; Kramer, Hahn et al., 1999; Kray et al., in press; Kray & Lindenberger, 2000). Nevertheless, both types of switching costs were still found after intensive practice, suggesting that switch costs are not a temporary phenomenon that disappears when tasks are automatized and that the range of plasticity is limited 8. In fact, general and specific switch costs seem to reflect constant executive control demands associated with the mechanic component of the cognitive system (Kluwe, 1997; Kray & Lindenberger, 2000). Importantly, the finding that a wide range of age groups showed improved task-switching abilities after training (Cepeda et al., 2001; Kramer, Hahn et al., 1999; Kray et al., in press; Kray & Lindenberger, 2000; Minear et al., 2002) provides evidence for a developmental reserve capacity with respect to executive control. Moreover, these results also show that training is a useful cognitive intervention for reducing age differences in task-set maintenance and selection. 8 Note, however, that these studies did not apply the testing-the limits approach. Hence, the potential for training-related benefits may not have been fully exhausted in these experiments. 60

69 Transfer of Training Transfer of Cognitive Training The literature reviewed in the previous section indicated that intensive training can reduce age-related deficits in executive control abilities, and that cognitive plasticity is considerable across a wide range of ages (see p. 57). However, aside from the mere investigation of training-related benefits, their transferability to new situations is of particular importance for the application of training programs in the clinical and educational context. Therefore, the following section focuses on the concept of transfer. After a short definition, the most important theoretical concepts are outlined, including models with respect to different types of transfer, the measurement of training and transfer benefits, and the mechanisms underlying these effects. The focus is on aspects most relevant for the present study. However, it should be noted that the majority of these theoretical considerations is based on research focusing on cognitive processes other than executive functioning, such as reasoning or memory, forming the basis for most of the work published in the field of transfer research (for reviews, see Klauer, 2001; Rosenbaum, Carlson, & Gilmore, 2001; Schmidt & Bjork, 1992). Also, differential aspects regarding transfer of training are discussed and criteria for the evaluation of training programs are illustrated. Afterwards, empirical evidence for the near and far transfer of cognitive training is reviewed with emphasis on executive control; and finally, the focus is on two methods for improving transfer effects, namely feedback and variable training. Theoretical Concepts Definition Transfer of training is generally defined as the effect of knowledge acquired in a previous situation on performance in a new situation (Mayer & Wittrock, 1996), that is, 61

70 Transfer of Training previously acquired knowledge affecting the way new information is handled (Cormier & Hagman, 1987). Especially researchers from the field of educational sciences have investigated transfer (e.g., De Corte, 1999; Mayer & Wittrock, 1996; for a review, see Klauer, 2001); some have even claimed that it is one of the most fundamental educational goals (Marini & Genereux, 1995). Therefore, the concept of transfer has often been linked to learning, that is, the human ability to flexibly use the appropriate knowledge and abilities to handle new, unfamiliar situations (Roth-van der Werf et al., 2002). In line with this, it is assumed that transfer is a central adaptive mechanism (Hesketh, 1997; Mayer & Wittrock, 1996), associated with intellectual abilities and intelligent human behavior (cf. Klauer, 1996, 1997). Types of Transfer Psychological and educational trainings usually aim at transfer to situations beyond the training context, particularly if the training is designed to improve cognitive abilities. A successful application of training-related improvements in new, unfamiliar situations is referred to as transfer. However, the term transfer is used quite inconsistently in the literature, frequently resulting in confusion (for a review, see Barnett & Ceci, 2002). Thus, despite the long tradition of training and transfer research, there is still no consensus whether transfer can be achieved, and if so, under which circumstances and to what extent. Some researchers rather deny that transfer is possible: The lesson learned from studies of transfer is that, if you want people to learn something, teach it to them. Don t teach them something else and expect them to figure out what you really want them to do (Detterman, 1993, p. 21). Other authors, however, point to conditions supporting the occurrence of transfer, such as the similarity between training and transfer tasks, proper instructions, and the type of training (e.g., De Corte, 1999; Klauer, 1989a, 1989b, 1996, 1998; Marini & Genereux, 1995; Mayer & Wittrock, 1996; for reviews, see Klauer, 2001; Rosenbaum et al., 2001; Schmidt & Bjork, 1992). 62

71 Transfer of Training In order to understand these quite contradictory positions, it is important to distinguish between different forms of transfer, such as near transfer (within tasks) and far transfer (across tasks) (Butterfield & Nelson, 1991; Detterman, 1993). Others have differentiated between specific (specific responses) and general (general principles) transfer (Detterman, 1993; Mayer & Wittrock, 1996; Novick, 1990), as well as between high-road (effortful and conscious) and low-road (automatic) transfer (Salomon & Perkins, 1989). Meanwhile, a couple of models have been proposed allowing to establish criteria for transfer and the quantification of transfer benefits (Barnett & Ceci, 2002; Hasselhorn & Hager, 1996). Two types of transfer are of particular importance for the present study, namely near and far transfer. Near transfer refers to situations that are structurally identical, but differ regarding details (e.g., the same kind of task structure, but different kind of stimuli), for instance, transfer of training in switching between tasks A and B to switching between tasks C and D. Far transfer, in contrast, refers to situations being different from the original one, such as transfer from task-switching training to other executive control tasks (e.g., the Stroop task) and even to other task domains, such as fluid intelligence. When it comes to near transfer, there is an ongoing debate on whether it is better described as learning (Brainerd, 1975; Roth-van der Werf et al., 2002; Salomon & Perkins, 1989) because learning is typically defined as a change in behavior in one situation as a consequence of repeated experience in a similar, but different situation (Bower & Hilgard, 1981). Klauer (1989b), in contrast, argued that normal learning merely is a special type of transfer. Consistently, Salomon and Perkins (1989) also point out that it is difficult to draw a hard line between mere learning and transfer. However, they claim that transfer is more likely to be mentioned when learning has side effects that exceed the usual expectations. 63

72 Transfer of Training Measurement of Transfer To date, it is widely accepted that an experimental design allowing conclusions regarding the effectiveness of a given training program and the subsequent transfer effects has to meet a number of preconditions. Although these preconditions seem relatively simple and logic at the first glance, surprisingly many training studies fail to meet these criteria (for a review, see Klauer, 2001). Therefore, these preconditions will be briefly summarized in this section. First, a training experiment must include at least two groups: One or more training groups and a control group. During training, the training group performs tasks requiring some cognitive ability to be trained. The control group, however, should perform tasks that are similar to those of the training group with respect to duration, number of sessions, contact with the experimenter, and the training equipment (e.g., a computer or other test material) and so forth, but these tasks should of course not train the specific ability practiced in the training group. Second, in order to examine transfer effects, the experimental design should not only include a transfer session after the training has been completed, but also a baseline measurement before training. Based on this baseline measurement, participants can be matched to the training and the control groups to prevent differences in baseline performance that often make the interpretation of transfer effects difficult. Therefore, the typical design to assess transfer of training is a pretest-training-posttest design. In these experiments, transfer is defined as the performance improvement at posttest relative to baseline performance at pretest. Thus, pretest and posttest sessions are usually identical, that is, they include the same tasks and measurements. In order to investigate longterm transfer effects, the pretest-training-posttest design can be completed with a follow-up measurement some time after the posttest (detail are provided below in this section). Assuming that transfer of training was found, it can be quantified along two dimensions: Effect size and effect range. Effect sizes are calculated to quantify the pretest- 64

73 Transfer of Training posttest performance improvements associated with a given type of training, while the effect range refers to the transfer distance, that is, the structural difference between training and transfer tasks: The more different the task structure and the underlying abilities of the transfer tasks are from the training tasks, the larger the transfer distance. However, some authors argue that effect size and effect range are not entirely independent. Salomon and Perkins (1989), for instance, refer to this phenomenon as power-generality trade-off. They assume that the more general a given training is, the weaker it is, so that training and transfer effects cannot be large and broad (i.e., affecting multiple domains) at the same time (cf. Weinert, 1987). According to this framework, training is most useful when effect size and effect range are on medium levels 9. And indeed, Klauer s (1989b) empirical findings support this notion by showing not only a negative correlation between effects size and effect range, but also a decreasing linear relationship between transfer effect size and transfer distance. Thus, according to this theory, it seems especially difficult to provide evidence for far transfer effects, because the transfer effect size decreases as a function of transfer distance. However, the verification of small effects requires large sample sizes, which can be problematic in training studies. Thus, in experiments with relatively small sample sizes only relatively large far transfer effects can be found reliably. In a meta-analysis including 302 studies of psychological, educational, and behavioral interventions, effects sizes were analyzed as a function of sample size (Lipsey & Wilson, 1993). In experiments with less than 50 participants (collapsed across experimental and control group), the mean effect size was.58. With a sample size between 50 and 100, the effect size decreased to.52, and with N > 9 Klauer (2001) has extensively examined the relationship between effect size and effect range for the domain of inductive reasoning. He argues that this relationship can easily be quantified: Assuming that N is the usefulness of training, p the probability that a trained/transferred ability is used, and w the proportion of explained variance of the trained ability relative to the total variance, then N = pw, if W = F(1-p). He further assumes that w is a monotone function of 1 - p, so that the usefulness of N is maximized if both p and w have medium values. 65

74 Transfer of Training 100, the mean effect size was.35 (the relevance of effect sizes for the evaluation of training programs is illustrated below). What Changes During Training? And What is Subsequently Transferred? For everyone interested in the transfer of training, the mechanism underlying trainingrelated improvements should be of interest. Put differently, how do training benefits emerge? When training related performance improvements were analyzed as a function of training time, usually logarithmic functions were found (cf. Klauer, 2001). That is, the largest performance improvements occurred after relatively little practice, and although further training constantly lead to better performance, the improvement constantly grew smaller (asymptotic approximation). Fitts (1954) referred to this type of curve as the law of practice. In order to explain this typical course of training-related improvement, different models have been proposed, most of them including assumptions regarding the automatization of the practiced abilities (Anderson, 1982; Logan, 1988; Schneider & Shiffrin, 1977). According to Anderson s (1982, 1987) analysis of skill acquisition, training improvements mainly result from two types of mechanisms: The first one (specialization) refers to practice-induced improvements at the level of individual operations that become tied to specific processes. The second mechanism (compilation) results in across-operation improvement, so that a sequence of several operations can be replaced with a single, higher-order operation. Thus, compilation would be reflected in improvements in the performance of a more complex task as a whole, leading to improved working-memory efficiency at retrieving task-relevant information and to better transition across operations. This concept is in line with Logan s (1988) theory that increasing automatization of the single component tasks involved in the performance of complex tasks leads to performance improvements after practice, and also fits the interpretation that practice effects observed after task-switching training have been attributed 66

75 Transfer of Training to task specific effects, such as the strengthening of stimulus-response rules or specific associations between cues and tasks (Jersild, 1927; Meiran, 1996; Rogers & Monsell, 1995). Over the years, various researchers have also dealt with the question which processes or abilities are actually transferred after training. More than 100 years ago, Thorndike and Woodworth (1901) published an experiment that was crutial for the development of transferrelated theories. They trained participants in estimating the area of triangles in different sizes. After performance had improved, the area of other geometric shapes, such as circles and trapezes, had to be estimated. However, participants previously trained to estimate the triangles did not perform better than untrained subjects. Based on this finding, Thorndike (1903) proposed his identical elements theory, suggesting that transfer depends on having shared elements in training and transfer tasks; the larger the number of such shared elements, the greater the likelihood that transfer will occur. In 1989, Singley and Anderson updated Thorndike s theory by suggesting that the relevant elements are rules applying both in training and transfer tasks, while Schumacher and Gentner (1988) pointed to the importance of a structural match between training and transfer task based on the systematicity and the transparency of the correspondence. MacKay (1982), Kramer, Strayer, and Buckley (1990), as well as Rickard and Bourne (1996) have discussed other versions of the identical elements theory. They assume that the shared elements can be abstract, that is, they pertain to whatever declarative or procedural knowledge is relevant for the task at hand. However, after an extensive review of previous empirical findings, Schmidt and Bjork (1992) suggested that the most important principle is the overlap of processes practiced during training and required during transfer. Importantly, they point out that this overlap of relevant processes does not necessarily mean that there is overlap of the training and transfer conditions. Put differently, this theory assumes that transfer can occur when the transfer tasks require one or more abilities that were trained in the practice phase, regardless of the structure underlying the 67

76 Transfer of Training transfer and training tasks. Thus, it is assumed that near as well as far transfer should be possible. How Long do Training and Transfer Effects Persist? When it comes to cognitive training, the goal is not only to enhance specific abilities, but also to achieve a generalization and long-term maintenance of these training related improvements. The question whether or not training can have longer-lasting effects has been a controversial issue for years (for a review, see Klauer, 2001). Some authors assume that training benefits, just like any other memory content, are eventually lost over time. Others, however, argue that training benefits increase over time, because the initial training triggers developmental progress. This head start can be used and extended, so that the developmental gap becomes wider ( scissor-effect ; cf. Coleman et al., 1966). Just as these theoretical positions, empirical evidence is ambiguous. Most studies including follow-up measurements after training and posttest were completed found decreasing transfer effects. Whether this transfer was still reliable at follow-up depended on the size of the initial training effect, the time interval between posttest and follow-up, and the sample size (Klauer, 2001). However, a number of studies also found long-term transfer effects in different age groups. Klauer (2001), for instance, reported relatively constant effects resulting from his inductive reasoning training program for up to 2 years (cf. Adey & Shayer, 1993; Burrmann, 1999). Klingberg and colleagues (2002b, 2005) reported the retention of training and transfer benefits after working-memory training in ADHD children up to a three-month follow-up. Results from the aging literature point into the same direction: Derwinger, Stigsdotter Neely, and Bäckman (2005) investigated the effects of memory training in older adults aged years and found that training-related gains were sustained over an 8-month interval. Likewise, a study from Vance et al. (2007) demonstrated that speed-of-processing training for older adults resulted in performance improvements that were robust over a 2-year period (for similar 68

77 Transfer of Training results after fluid-intelligence and attention training, see Blieszner, Willis, & P. B. Baltes, 1981; Plemons, Willis, & P.B. Baltes 1978; Willis, Cornelius, Blow, & P. B. Baltes, 1983). Most striking, however, are the results involving the SIMA-program, designed to maintain and support independent living in older age. Several studies showed that combined memory and psychomotor training had positive long-term effects on cognitive performance for up to five years (e.g., Oswald, 2004; Oswald, Hagen, & Rupprecht, 1998; cf. Ball et al., 2002). Empirical evidence for long-term transfer (more than 2 months) of executive control training in a sample of autistic children comes from Fisher and Happé (2005), and from Kramer, Larish, and colleagues (1999) for younger and older adults (both studies are described below, see p. 72). In sum, these results support the view that training and transfer benefits can indeed be maintained across a longer period of time. Who Benefits from Training? Are Training and Transfer Benefits Predictable? Training programs have become a frequently applied type of intervention in populations with cognitive deficits associated with a wide range of conditions, such as schizophrenia, head trauma, multiple sclerosis, dementia, and normal aging (for reviews, see Bissig & Lustig, 2007; Royall et al., 2002), and they can lead to significant changes in behavior and brain function (e.g., Ball et al., 2002; Jennings, Webster, Kleykamp, & Dagenbach, 2005; Nyberg et al., 2003; Rueda et al., 2005). However, although many training programs are successful at the group level, individual differences with respect to the degree of improvement are relatively large (see Bissig & Lustig, 2007). Therefore, the differential aspects of training and transfer are of great interest, especially for the adaptation of training programs to populations with special needs. Specifically, the question is whether individual training and transfer effects can be predicted. Again, there are two controversial theoretical views. The first idea is that training has compensatory effects in the sense that low-performing participants benefit more than high-performing participants. Put differently, the worse subjects perform 69

78 Transfer of Training prior to training, the larger the training and transfer benefits. According to this theory, training would result in reduced within-group variance. The second position is referred to as Matthew [Matthäus] -effect 10, assuming that better performers benefit more from training. If this was true, the within-group variability should be increased after training. Unfortunately, empirical findings regarding training-related changes in variance are very unclear (cf. Ackerman, 1987; Bissig & Lustig, 2007; Klauer, 2001). Some studies showed that the training benefits were smallest for those individuals who needed them most, that is, lower initial cognitive status and more advanced age were associated with smaller trainingrelated improvements (e.g., P. B. Baltes & Kliegl, 1992; Verhaeghen et al., 1992; Yesavage, Sheikh, Friedman, & Tanke, 1990). However, there also is considerable evidence from taskswitching studies pointing to larger training benefits in children and older adults compared to younger adults (e.g., Cepeda et al., 2001; Kramer, Hahn, et al., 1999; Kray & Lindenberger, 2000; Minear et al., 2002). Given that these variance changes are critical for the measurement of inter-group differences, they seem particularly problematic. Moreover, they also affect the calculation of status-benefit correlations often used to provide evidence for one of the contradictory positions (compensatory vs. Matthew -effects). While the first one predicts negative correlations between status and training/transfer benefit, the latter one assumes that these correlations are positive 11. Evaluation of Training and Transfer Effects The effectiveness of cognitive training programs can be evaluated, and most importantly, compared to other types of training or other training conditions. The consequences for the application of a given type of training are obvious: If we know under 10 This effect was named after Matthew 13, 12: For whoever has, to him more shall be given. 11 It should be noted that the simple status-benefit correlation is often lower than the true correlation because the pretest error enters the status-benefit correlation twice (Bereiter, 1967). Klauer (2001) suggested using a complex design including parallel versions of the pretest and posttest measures and the application of the Lord (1967) correction factor. 70

79 Transfer of Training which circumstances transfer is likely to occur, we can systematically vary training characteristics in order to optimize the training and to adapt it to the needs of the participants. Within his research on cognitive training, Klauer (2001) proposed a simple multi-step strategy for the evaluation of training programs. His focus was on the question whether the training not only improved performance on the training task, but also the underlying cognitive abilities. The evaluation program consists of several steps; the most important ones are briefly discussed in this section and illustrated in Figure 6. Did training improve performance? Yes No Is there transfer to tasks also depending on the trained ability? Yes No Can the training benefit be explained otherwise? No Yes Are there long-term effects? Yes No accept reject the hypotheses of training validity Figure 6: Klauer s strategy for the evaluation of training programs. Figure adapted from Klauer (2001). Step 1: Confirm that performance has improved. This first step, of course, is a given: If our goal is to train a specific ability, then we have to analyze whether performance is actually improved after training. However, how much improvement is necessary in order to consider training effective? In the literature, two criteria are frequently applied: (1) Klauer (2001) suggested to that the effect size of the training effect should be at least.30, while (2) other 71

80 Transfer of Training authors calculated the proportion of participants showing training-induced benefits (cf. Derwinger et al., 2003), which should be larger than 50%. Step 2: Analyze transfer to other dependent variables. Klauer (2001) assumed that after successfully training a specific ability, this training-related benefit should influence performance on other tasks relying on this ability. Thus, near and far transfer should be examined. Step 3: Exclude alternative explanations. In order to exclude alternative explanations for improvements at the end of training, such as motivational or attendance effects, appropriate experimental designs are required, including training groups that only differ regarding one relevant aspect. Step 4: Show long-term effects. Finally, long-term training effects should be examined (cf. Belmont & Butterfield, 1977; Hasselhorn, 1987) in order to prove that the training resulted in lasting improvement of the respective cognitive ability. However, given that the expression long-term is a bit vague and the maintenance of training benefits depends on the frequency the training ability is used after training, Klauer (2001) suggests that the training effect should still be present three months after training has ended. In sum, it seems not only important to consider training-related benefits and their transferability on the level of group mean performance, but also to investigate the effect sizes and the proportion of transfer as well as the questions how long training and transfer benefits persist and whether they can be predicted. Empirical Evidence for Near and Far Transfer of Executive Control Training While most studies with respect to transfer of training have focused on inductive reasoning (e.g., Brown & Kane; 1988; Ferrara, Brown, & Campione, 1986; Roth-van der Werf et al., 2002; for reviews, see Hasselhorn & Hager, 2006; Klauer, 1995, 2001), problem solving (e.g., Butterfield & Nelson, 1991; Crisafi & Brown, 1986; Holyoak, Junn, & Billman, 1984; 72

81 Transfer of Training Novick, 1988, 1990), fluid abilities (e.g., P. B. Baltes, Kliegl, & Dittman-Kohli, 1988; P. B. Baltes, Sowarka, & Kliegl, 1989; Edwards et al., 2005; Hayslip, 1989) and memory strategies (e.g., Derwinger et al., 2003; Jennings et al., 2005; Pressley, 1982; Ringel & Springer, 1980; Ryan, Ledger, & Weed, 1987; Singer et al., 2003), empirical evidence for the transfer of training in executive control is scarce, and studies adopting a lifespan perspective do not exist. However, there are a few studies focusing on childhood development or cognitive aging indicating that transfer of executive control training can be achieved. In the following section, these studies are reviewed with emphasis on experiments including different age groups. Transfer of Executive Control Training in Childhood Klingberg and colleagues (2002b) examined 7 to 15-year-old children with attentiondeficit and hyperactivity disorder (ADHD) by means of a training paradigm including adaptively adjusted difficulty levels. They demonstrated that working memory training not only improved performance in other working-memory tasks (near transfer), but also performance in other tasks assumed to rely on executive control, such as the Stroop task, or to fluid intelligence tasks (far transfer) (cf. Klingberg et al., 2005). In the same study, similar positive effects of working memory training were also obtained in a group of younger adults (for near and far transfer of memory training in older adults, see Jennings et al., 2005). In a later study, Klingberg and colleagues (2005) showed that the working-memory training also resulted in a reduction of the parent-rated inattentive symptoms of ADHD, and that the transfer benefits were still found at a follow-up three months later. Although studies including preschool children usually do not apply the task-switching paradigm (see p. 45), there is evidence for the transfer of executive control training in this age group. Kloo and Perner (2003), for instance, investigated the development of executive control and theory of mind in 3 to 4-year-olds by means of the DCCS (Dimensional Change Card Sort, see p. 45) and the false-belief task. They found that DCCS training improved performance on 73

82 Transfer of Training the false-belief task and vice versa, suggesting a close developmental link between executive abilities and theory of mind. Similarly, Fisher and Happé (2005) trained autistic children (6 15 years of age) either in theory of mind tasks or in executive control. Participants were tested before training, after training, and at a two-month follow-up. Results showed performance improvements in theory of mind tasks after both types of training that were still present at follow-up. Rueda and colleagues (2005) recently examined the efficiency of attentional networks after 5 days of training across the ages of 4 to 6 years. The training included different executive exercises, among them a spatial navigation task, a Stroop-like task and an inhibitory control task. After training, both age groups showed more mature performance regarding behavioral scores of the executive attentional network (measured by the Attention Network Test, basically a modified version of the flanker paradigm), ERP measures and intelligence test scores. Thus, training in executive tasks generalized to other executive tasks (near transfer), as well as to aspects of intelligence quite remote from the training tasks (far transfer). In another study, Dowsett and Livesey (2000) examined effects of experience on the development of inhibitory control by exposing young children (3 5 years of age) to training on tasks requiring executive functioning. They showed that practice in a go/nogo task improved performance on that same task; but interestingly, children showed even more improvement in the go/nogo task when they were trained with other executive tasks, namely a modified version of the WCST and a card change task. Hence, inhibitory control was improved via training with tasks requiring executive processes other than response control (far transfer). It should be noted, however, that the practice/training variable in this study was confounded with a feedback/no feedback variable; the positive training effects thus may be partly due to the explicit feedback in the training condition (feedback effects are addressed below). Nevertheless, the results reported in this study point into the same direction as those obtained by Rueda et al. (2005). 74

83 Transfer of Training Transfer of Executive Control Training in Older Age Several studies have shown that training can substantially reduce older adults deficits in dual-task performance (Bherer et al., 2005; Kramer et al., 1995; Kramer, Larish, et al., 1999). Kramer and colleagues (1995) found that younger and older adults can learn to effectively coordinate the performance of two tasks and that older adults benefited more from training than younger adults did. Moreover, the training-related benefits transferred to a novel dual-task situation (near transfer) and were retained for up to 2 months. Thus, executive control skills, such as the coordination of multiple tasks, could be substantially improved in both younger and older adults. However, using a PRP design (psychological refractory period, see p. 39), Maquestiaux and colleagues (2004) showed that extensive practice alone did not foster the development of efficient dual-task strategies. They assumed that an improvement in dual-task performance may only be observed when participants are explicitly trained to perform multiple tasks, for instance by means of adaptive feedback (effects of feedback on transfer are discussed below) or prioritization strategies (cf. Kramer et al., 1995; Kramer, Larish, et al., 1999). Therefore, Bherer et al. (2005) examined the extent to which age-related deficits in dual-task performance can be moderated by training in younger and older adults. Subjects were provided continuous adaptive feedback and priority instructions (indicating which task to respond to first) during the training sessions. Moreover, the authors assessed whether acquired task coordination skills generalized to untrained stimuli, within as well as acrossmodalities (i.e., from auditory to visual stimuli). Results revealed that both younger and older adults showed training-related benefits in terms of response speed, general switch costs, specific switch costs, and accuracy, with greater improvements for older adults on the level of accuracy. Together with a number of findings from Kramer and colleagues (1995, 1999) (see also Cepeda et al., 2001; Kray et al., in press; Kray & Lindenberger, 2000; Minear et al., 2002), this argues against the often-reported observation of reduced training benefits for older 75

84 Transfer of Training adults compared to younger adults (Baltes & Kliegl, 1992; Lindenberger & Baltes, 1995). Importantly, in both age groups the training-related improvement with respect to general and specific switch costs generalized to new task combinations involving new stimuli, within as well as across task modalities (near transfer) (cf. Kramer et al., 1995). Thus, the transfer data suggest that participants learned a somewhat generalizable set of skills that entailed the ability to prepare and to perform multiple tasks as well as the ability to perform multiple tasks concurrently (Bherer et al., 2005, p. 707). Finally, Minear (2004) reported a series of five experiments focusing on the transfer of task-switching training. Given that her study is particularly important for the present experiment, it will be reported more extensively. In order to assess the transfer of taskswitching training, participants performed an intensive two-day task-switching training (i.e., mixed-task blocks only). This group was compared to a control group performing the same amount of single-task training (i.e., single-task blocks only). The first two experiments showed a reduction of general and specific switch costs after training. However, only the improvement in general switch costs transferred to a new, untrained switching task. This finding was consistent across an internally cued as well as a cue-based switching paradigm. In three further experiments, Minear (2004) investigated whether the effects of training are specific to the context of a particular paradigm, that is, whether training by means of a predictable paradigm transfers to a random paradigm and vice versa. Results replicated the findings from the first two experiments by showing transfer on the level of general switch costs within one paradigm, but no evidence for transfer from one training regimen to another, indicating that the transfer benefits seem to be limited to the paradigm in which one is trained. The author agued that participants in both paradigms improved during training due to strategic shifts in goal selection; however, this change may have been associated with different trial type expectancies after practice in the random group, while the performance improvements in the predictable paradigm may be due to improved task preparation. 76

85 Transfer of Training Finally, there is another study from Minear and colleagues (2002), which is particularly important for the present study. The authors examined age differences between younger and older adults in the transfer of task-switching training - compared to the training of the two single tasks - to a similar switching task by means of an internally cued switching paradigm. Both younger and older adults showed a substantial reduction of general switch costs after 2 days of training. In contrast to the training of the two single tasks, task-switching training resulted in the transfer of these training gains (i.e., a reduction of general switch costs) to a non-trained similar switching task (see Figure 7). This transfer effect was more pronounced for older adults than for younger adults. This study provides evidence that executive control training can transfer to non-trained but structural similar tasks (i.e., near transfer) in younger and older adults (cf. Bherer et al., 2005; Kramer et al., 1995; Kramer, Larish, et al., 1999). Thus, it seems that training can reduce general and specific switch costs and that particularly the benefits on the level of general switch costs can be transferred to new, non-trained switching tasks in different age groups. log RT difference score switch-pure General Switch Costs (log RT) Task-Switching Training Young young Older old adults adults Single-Task Training Young young Older old adults adults Pre-Test Post-Test Figure 7: Reduction of general switch costs from pretest to posttest as a function of age group (younger adults, older adults) and training group (single-task training, task-switching training). Figure adapted from Minear, Shah, and Park (2002). To sum up, there is a growing body of evidence supporting the view that after intensive executive control training, near transfer to structurally similar tasks (Bherer et al., 2005; 77

86 Transfer of Training Kramer, et al., 1995; Kramer, Larish et al., 1999; Minear et al., 2002) and even far transfer to structurally dissimilar tasks (Dowsett & Livesy, 2000; Fisher & Happé, 2005; Jennings et al., 2005; Klingberg et al., 2002b; Klingberg et al., 2005; Kloo & Perner, 2003; Rueda et al., 2005) is possible in different age groups. However, there is a lack of studies systematically investigating the occurrence of near and far transfer effects based on lifespan samples. Promoting the Transfer of Cognitive Training After reviewing empirical evidence for transfer of training, this last paragraph focuses on conditions increasing the occurrence and the amount of transfer. Previous studies have identified a number of potential factors, such as the degree of similarity between training and transfer tasks (e.g., Crisafi & Brown, 1986; Gentner & Toupin, 1986; Holyoak et al., 1984; Roth-van der Werf et al., 2002; Schumacher & Gentner, 1988; but see Novick, 1990), individual differences in intellectual abilities (e.g., Ferrara et al., 1986; Klauer, 1996, 1997), the time interval between training and posttest (e.g., Hayslip, 1989), and the type of instruction during training and posttest (e.g., Brown & Kane, 1988; Crisafi & Brown, 1986; Holyoak et al., 1984; see also Reder, Charney, & Morgan, 1986; Ryan et al., 1987). However, here the focus is on two manipulations particularly important for this study, namely feedback and training variability. Feedback and Transfer The modulation of age differences in task-switching abilities by means of verbal processes has been discussed above (see p. 52). Specifically, verbal self-instructions performed during task switching strongly supported the maintenance and selection of task sets (Kray et al., in press). This benefit was most pronounced for younger children and older adults, so that age differences in general switching costs were reduced. These results suggest that verbal self-instructions are a useful tool for reducing action-control deficits in childhood and 78

87 Transfer of Training older age. However, although these findings point to an important function of verbal processes for action control, effects of verbal self-instruction training are not always easily transferable to other situations. Especially older adults and children often fail to spontaneously use verbal strategies in new task situations (Flavell, Beach, & Chinsky, 1966; Meichenbaum, 1974; Ringel & Springer, 1980; cf. Salomon & Perkins, 1989). The tendency that young children do not use language at appropriate points in the task situation, thereby precluding facilitation effects, has been referred to as deficiency hypothesis (Flavell et al., 1966; for a review, see Bjorklund, Miller, Coyle, & Slawinski, 1997). It has been suggested that this speech production deficiency may be due to the absence of knowledge about the value of rehearsing, that is, a lack of awareness regarding the effectiveness of strategy use. Likewise, Salomon and Perkins (1989) pointed to the problem of inert knowledge and suggested that explicit instructions emphasizing the usefulness of transfer strategies can foster positive transfer. One way to raise the children s awareness of a strategy s effectiveness is to provide explicit feedback regarding task performance and strategy value. Thereby, an evaluation process is initiated and awareness of the strategy s effectiveness is increased. In fact, when provided feedback, children are more likely to use a strategy in a new task situation and to show positive transfer effects (Kennedy & Miller, 1976; Ringel & Springer, 1980; cf. Dowsett & Livesey, 2000). Kennedy and Miller (1976), for instance, trained children to verbally rehearse in a serial recall task, resulting in superior task performance. Given the option of rehearsing, only those children provided feedback regarding the strategy s value persisted in using it. Similarly, Ringel and Springer (1980) explored children's transfer of learning strategies, assuming that a self-monitoring process is essential for evaluating one's own level of performance and the effectiveness of various mnemonic strategies. In fact, only children provided with feedback and strategy instruction continued to rely on the strategy when faced with transfer tasks, and in addition showed positive transfer effects (cf. Dowsett & Livesey, 2000). 79

88 Transfer of Training Training Variability and Transfer When researchers examine the transfer of training, they usually ask subjects to engage in practicing some task in a training phase, in which one or more independent variables are manipulated. The nature of the independent variable can be of various types, such as the type of instructions, feedback, or training tasks. Performance on the training tasks is typically analyzed as a function of practice for the different levels of the independent variable. The logic of this kind of paradigm is that those training conditions resulting in most effective performance during this training phase also are the most effective for learning the respective tasks. However, there is considerable evidence indicating that conditions facilitating performance during training are not always the most effective conditions to support the acquisition of a generalizable skill. In contrast, manipulations decreasing the speed of skill acquisition during training can support its long-term goals (for reviews, see Rosenbaum et al., 2001; Salomon & Perkins, 1989; Schmidt & Bjork, 1992). Among these types of training is variable training, that is, exposing subjects to different material during practice. According to Rosenbaum and colleagues (2001), using the same materials during training (constant training) leads to better performance at the end of training but to worse performance in later transfer tests. By contrast, exposing learners to different materials (variable training) leads to worse performance at the end of training but better performance in later tests, and even to more transfer. The long-term benefit of variable training has been observed with perceptualmotor, verbal and intellectual tasks (for reviews, see Rosenbaum et al., 2001; Schmidt & Bjork, 1992; Shapiro & Schmidt, 1982). Thus, the time at which the effectiveness of training is assessed seems to be critical, especially because faster and easier training conditions are often considered to produce more effective learning (see also Bjork, 1994). When it comes to age differences regarding the influence of variable training on the amount of transfer, empirical evidence is scarce. Sanders, Gonzalez, Murphy, Pesta, and 80

89 Transfer of Training Bucur (2002), for instance, have shown that high variability training in mental calculation tasks resulted in inferior performance at the end of training, but in transfer to non-trained tasks in young adults. In contrast, there was no difference in performance between the high and the low-variability training condition in older adults. With respect to executive functions, Kramer et al. (1995) showed that dual-task abilities could be trained and transferred in younger and older adults (see p. 57). Interestingly, a greater improvement was found when participants were trained in a variable-priority condition compared to a fixed-priority condition, supporting the view that learning to modulate attention may be crucial in the acquisition of task-coordination skills (cf. Gopher, Weil, & Siegel, 1989). Compatible with these findings is the view that the amount of transfer varies as a function of automatization (cf. Frensch & Sternberg, 1989; Sternberg & Frensch, 1989). Specifically, it is assumed that transfer of training is more likely the more automatized the trained abilities have become. However, this account also assumes that transfer may be impaired if the degree of automatization becomes too large, because subjects fail to flexibly adapt to new task demands. Put differently, if one practices one task or one strategy over and over again, one may fail to adapt this strategy to the changing demands of new, unpracticed task situations. In sum, there is evidence suggesting that, at least in children, feedback regarding the strategy value can promote transfer. When it comes to adults, variable training can modulate performance in the training phase as well as the amount of transfer after training. Therefore, the present study investigated age differences in the influence of feedback and training variability on the transfer of task-switching training. 81

90 General Summary General Summary In the first section of the theoretical part, different concepts of executive functions and their lifespan development have been reviewed. The term executive functions refers to higher-level processes organizing lower-level processes in order to regulate behavioral activity allowing individuals to optimally adapt to continuous changes in the environment (cf. Baddeley, 2000; Duncan, 1995; Logan 2000; Norman & Shallice, 1986; Roberts & Pennington, 1996; Smith & Jonides, 1999). While most traditional models (e.g., Baddeley, 1986, 2000; Norman & Shallice, 1986) suggested the existence of a central control system, it is widely accepted these days that executive control is not a unitary construct, but consists of several separable control components, such as shifting, updating, and inhibition (cf. Fisk & Sharp, 2004; Huizinga et al., 2006; Kray & Lindenberger, 2000; Miyake et al., 2000). Empirical evidence indicates that executive control functions are closely linked to intellectual functioning (for reviews, see Kray & Lindenberger, 2007; Lindenberger & Kray, 2005), and that several executive abilities, such as interference control or the ability to maintain and select task-sets on the one hand, and the mechanic component of intellectual abilities on the other hand, have similar developmental trajectories, namely a marked increase from childhood to adolescence followed by a constant decline in older age (for a review, see Craik & Bialystok, 2006). When it comes to the measurement of executive control, the task-switching paradigm (cf. Kray & Lindenberger, 2000; Rogers & Monsell, 1995; for a review, see Monsell, 2003) has become a well-established instrument to investigate executive control across a wide range of ages. In task-switching studies, participants are usually instructed to perform two simple tasks A and B, either in single-task blocks, in which only task A or B have to be performed separately, or in mixed-task blocks, in which subjects have to switch between both tasks. This design allows calculating two types of switch costs: General switch costs, measuring the ability to maintain and select task sets, and specific switch costs, referring to the ability to switch 82

92 General Summary Goschke, 2004; Miyake et al., 2004; Saeki & Saito, 2004). In line with this, particularly children and older adults can use verbal self-instructions to compensate for age-related executive control deficits on the level of task-set selection and maintenance (Kray et al., in press; see also Kray et al., 2004). Another way to modulate age-differences in task-switching abilities seems to be intensive training. A number of task-switching studies indicated that both types of switching costs could be reduced - but not eliminated - after practice, especially in children and older adults (Cepeda et al., 2001; Kray et al., in press; Kray & Lindenberger, 2000; Kramer, Hahn, et al., 1999; Minear et al., 2002), again pointing to compensatory effects in age groups characterized by marked executive deficits. However, aside from investigating mere training-related benefits, their transferability to new, untrained situations is of particular importance for the application of training programs in the clinical and educational context. Therefore, the third section of the theoretical part was dedicated to the concept of transfer, that is, a successful application of training-related benefits in a new, unfamiliar situation. Despite the long tradition of training and transfer research, there is still no consensus whether and to which extent transfer can be achieved (for a review, see Barnett & Ceci, 2002). Most authors differentiate between several forms of transfer (e.g., Butterfield & Nelson, 1991; Dettermann, 1993; Mayer & Wittrock, 1996; Salomon & Perkins, 1989; Novick, 1990). For the purpose of the present study, two types of transfer are particularly important: Near transfer, referring to situations that are structurally similar, but differ regarding perceptual details (e.g., transfer of task-switching training with tasks A and B to tasks C and D), and far transfer, referring to situations with dissimilar task structure (e.g., transfer of task-switching training to the Stroop task). Transfer is usually measured by means of pretest - training - posttest designs, in which transfer is defined as performance improvement at posttest relative to baseline performance at pretest. In order to explain the processes underlying the occurrence of transfer effects, a number of theoretical models have been put forward dealing with the question what processes 84

93 General Summary and abilities change during training and can subsequently be transferred. Anderson (1982, 1987), for instance, assumes that training-related benefits emerge in two steps: First, specific operations are optimized, and second, several of these specific operations are replaced with a single, higher-order operation, resulting in performance improvements in complex tasks. When it comes to the transfer of training benefits, traditional models assumed that the larger the number of shared elements between training and transfer tasks is, the more likely transfer occurs ( identical elements theory ; Thorndike, 1903; see also Singley & Anderson, 1989). However, in the light of more recent findings, it seems more likely that transfer occurs when the transfer tasks require one or more abilities that had been trained before, regardless of the structure underlying these tasks (for a review, see Schmidt & Bjork, 1992). Training programs have become a frequently applied type of intervention in populations with executive deficits associated with a wide range of conditions (for reviews, see Bissig & Lustig, 2007; Royall et a., 2002), leading to significant changes in behavior and brain function (e.g., Ball et al., 2002; Jennings et al., 2005; Nyberg et al., 2003; Rueda et al., 2005). However, individual differences with respect to the degree of improvement are relatively large (see Bissig & Lustig, 2007). Therefore, the differential aspects of training and transfer are of great interest, especially for the adaptation of training programs to populations with special needs, such as children and older adults. However, evidence regarding the prediction of training benefits is ambiguous some studies found that lower initial cognitive status predicted less training benefits (Verhaeghen et al. 1992; Yesavage et al., 1990), while others found that it was associated with larger training benefits (e.g., Cepeda et al., 2001; Kramer, Hahn, et al., 1999; Kray & Lindenberger, 2000; Minear et al., 2002). Previous research indicated that the transferability of cognitive training seemed to be limited. Positive transfer is most often confined to near transfer, that is, performance in new, non-trained tasks only improves when these tasks are structurally similar to the training tasks (near transfer), but not if they are structurally dissimilar (far transfer). Thus, it seems that 85

94 General Summary cognitive training primarily taps into task-specific components that cannot be transferred to new structurally dissimilar tasks (e.g. Derwinger, et al., 2003; Klauer, 1989a, 1989b; Roth-van der Werf et al., 2002; but see Kramer et al., 1995). However, a number of recent studies suggest that a larger generalization of training-related benefits can be achieved in different age groups. For instance, working memory as well as executive control training in children was transferable to structurally similar and dissimilar tasks (Dowsett & Livesey, 2000; Fisher & Happé, 2005; Kloo & Perner, 2003) as well as to aspects of intelligence quite remote from the training tasks (Klingberg et al., 2002b; Klingberg et al., 2005; Rueda et al., 2005). Evidence for the transfer of executive control training in adults mostly comes from dual-task studies, indicating that younger as well as older adults can transfer training-related benefits to novel dual-task situations (Kramer et al., 1995; Kramer, Larish, et al. 1999). Meanwhile, this finding has also been replicated for task-switching performance, that is, younger and older adults were able to transfer training-related benefits on the level of switch costs to new untrained switching tasks (Bherer et al., 2005; Minear et al., 2002). Finally, reviewing the literature also suggests that a number of experimental manipulations can serve to manipulate the occurrence and the amount of transfer (for reviews, see Rosenbaum et al., 2001; Schmidt & Bjork, 1992). Most important for this study is the finding that explicit feedback indicating the value of certain training strategies, such as verbal self-instructions, can foster transfer in children (Kennedy & Miller, 1976; Ringel & Springer, 1980; for e review, see Bjorklund et al., 1997). Moreover, transfer in adults can be increased by means of variable training, that is, exposing participants to different training tasks or conditions during training (Kramer et al., 1995; Sanders et al., 2002; for reviews, see Rosenbaum et al., 2001; Schmidt & Bjork, 1992; Shapiro & Schmidt, 1982). The aim of the present study was to examine age differences in the near and far transfer of task-switching training and its modulation by different types of training. Based on relevant previous findings, the research design along with the hypotheses is presented in the next section. 86

95 Statement of Problem 3. Statement of Problem and Research Hypotheses The review of the literature provided in the previous section presented considerable evidence for age-related changes in executive functioning, pointing to the multidirectionality and the multidimensionality of cognitive development across the lifespan (cf. Baltes, 1990). Most important in the context of the present study are findings with respect to age differences in task-switching abilities (Bherer et al., 2005; Cepeda et al., 2001; Crone, Bunge et al., 2006; Crone et al., 2004; Karbach & Kray, 2007; Kray, 2006; Kray et al., in press; Kray et al., 2004; Kray et al., 2002; Kray & Lindenberger, 2000; Mayr, 2001; Meiran et al., 2001; Reimers & Maylor, 2005). Although a number of previous experiments have assessed the effect of intensive task-switching training (e.g., Allport et al., 1994; Cepeda et al., 2001; De Jong, 2001; Jersild, 1927; Kramer, Hahn, et al., 1999; Kray et al., in press; Kray & Lindenberger, 2000; Minear, 2004; Minear et al., 2002; Rogers & Monsell, 1995), the transferability of these training-related gains to new, untrained task situations has widely been neglected (but see Minear, 2004; Minear et al., 2002). Even though one task-switching study indicated that transfer of training-related benefits is possible in younger and older adults (Minear et al., 2002), there is no such evidence based on a sample including children as well as younger and older adults within one study, thereby allowing the direct comparison of age differences with respect to near transfer and a systematic investigation of far transfer based on the same tasks and paradigms within these age groups. Given that the transferability of training-related gains in executive functioning is especially important not only for experimental psychology, but even more for the application of cognitive training programs in the clinical and educational context, it is certainly promising to investigate the conditions mediating and supporting the occurrence of transfer. In addition, it seems important to examine whether different groups of participants show differential benefits associated with different types of training, so that training programs can be adapted to the 87

96 Statement of Problem needs of different populations. Therefore, the general aim of this study was to examine age differences in the near and far transfer of task-switching training in children (8 10 years of age), younger adults (18-27 years of age), and older adults (63 76 years of age) under different training conditions. More specifically, the first goal was to investigate age differences in the near transfer of task-switching training to a similar switching task and the far transfer of task-switching training to other executive tasks (Stroop task, working memory) and to another task domain (fluid intelligence). The second aim was to investigate whether the type of training modulated the amount of transfer. Since previous findings showed that particularly children and older adults can use verbal self-instructions to compensate for age-related deficits in general switching abilities (Kray et al., in press), the aim of the present study was to investigate whether these benefits associated with the verbalizations can be transferred to a similar switching task and to other tasks relying on verbal rehearsal processes (verbal working memory) after training. However, it seems that at least in children, the transfer of verbal strategies can be supported by means of explicit feedback indicating the value of the verbalization strategy during training (cf. Bjorklund, et al., 1997; Flavell et al., 1966; Kennedy & Miller, 1976; Ringel & Springer, 1980). Therefore, the present study also assessed age differences in the influence of feedback on the amount of task-switching and verbal selfinstruction transfer. Finally, we know that transfer in adults can be increased by means of variable training (i.e., exposing participants to different material or changing task demands during training) (Kramer, Larish, et al., 1999; Sanders et al., 2002; for reviews, see Rosenbaum et al., 2001; Schmidt & Bjork, 1992). Therefore, another goal of this study was to investigate the influence of variable training tasks (i.e., different training tasks in each training session) on the amount of transfer. 88

97 Statement of Problem Overview of Study Design In order to investigate age differences in the near and far transfer of task-switching training, a pretest - training - posttest design including four sessions of intensive training was applied to children, younger adults, and older adults. An alternating-runs paradigm (i.e., without external task cues) was chosen to increase participants reliance on internal verbal cueing during task performance (see p. 52). At pretest and posttest, participants performed a switching task similar to the one applied during training in order to investigate near transfer of task-switching training to a similar switching task. To examine far transfer of task-switching training, they also performed a battery of cognitive tasks at pretest and posttest, including other executive measures (the Stroop task as well as verbal and visuospatial working memory tasks) and measures of fluid intelligence. Finally, to show that task-switching training does not result in transfer to tasks not relying on executive control, the cognitive test battery also included a number of control tasks (verbal sped, perceptual speed, and knowledge). However, the present study also aimed at identifying the optimal training conditions, that is, the training conditions yielding the largest transfer effects in each one of the age groups. Therefore, participants were assigned to one of five training conditions. The first two conditions were included to examine the mere transfer of task-switching training. Specifically, the first group - serving as a control group - was only trained in single-task performance (i.e., single-task blocks), so that the training of executive control processes should be low. In contrast, the second group was only trained in task switching (i.e., mixed-task blocks), so that training in executive control should be intense (cf. Minear, 2004; Minear et al., 2002). Comparing the posttest performance of these two groups indicated whether participants in different age groups were generally able to transfer task-switching training benefits to other tasks. The remaining three training groups served to investigate whether this transfer can be promoted by means of different training conditions. Therefore, the switching training was 89

98 Statement of Problem combined with verbal self-instructions, feedback (indicating the value of this verbalization strategy), and training variability (i.e., different training tasks in each training session) (for details, see p. 118). Research Predictions The presentation of the research predictions is divided into three parts: The first part focuses on the task-switching training sessions, the second one on age differences in near transfer of task-switching training to a similar switching task, and the third one on age differences in far transfer to other executive control tasks and other task domains. For each of these parts, empirical evidence is briefly subsumed and the corresponding predictions are presented. Age Differences in Task-Switching Training Benefits Given that the focus of the present study was on age differences in the near and far transfer of task-switching training rather than on training-related benefits in task-switching abilities, this part is kept relatively short. However, it should be noted that although it primarily is a control analysis, the inspection of the training data is very important for the interpretation of the subsequent transfer effects. Thus, the aim was to investigate age differences in the effects of the intensive four-session task-switching training on specific switching abilities as well as the role of training type for these training-related benefits. The present study included four task-switching training groups: One was only trained in task-switching, the second one additionally received verbal self-instruction training, and for the remaining two groups, the task-switching and verbal self-instruction training was either combined with feedback indicating the value of the verbalization strategy, or with training variability (for details, see p. 90

99 Statement of Problem 118). The training sessions for all four task-switching training groups completely consisted of mixed-task blocks, so that only specific switch costs could be inspected 12. Prior research indicates that specific switch costs can be reduced - but not eliminated - by training in younger and older adults; however, there were no age differences in the amount of training-related benefits between younger and older adults (Bherer et al., 2005; Kramer, Hahn, et al., 1999; Kray & Lindenberger, 2000; Minear et al., 2002). For children, evidence for a reduction of specific switch costs after task-switching training is lacking. Regarding the modulation of switch costs by means of verbal processes, prior evidence has shown that general switch costs (Baddeley et al., 2001; Saeki & Saito, 2004) are more sensitive to articulatory suppression than specific switch costs (Bryck & Mayr, 2005). So far, there is evidence for a reduction of general switch costs, particularly in children and older adults, when verbal self-instructions (i.e., task-relevant verbalizations) are performed during task preparation (Kray et al., in press; see also Goschke, 2000). However, the influence of the verbalizations on specific switch costs in prior studies was less pronounced. Finally, the question is whether feedback and training variability have an effect on training-related benefits on the level of specific switching abilities. Feedback indicating the value of the verbalization strategy has been shown to support the occurrence of transfer benefits (Kennedy & Miller, 1976; Ringel & Springer, 1980), but not to modulate training performance. However, evidence from other experimental paradigms indicates that trainingrelated benefits are modulated by training variability in young adults. More specifically, variable training is known to slow down skill acquisition during training, but to be superior in terms of retention and transfer afterwards (Sanders et al., 2002; for reviews, see Rosenbaum et al., 2001; Schmidt & Bjork, 1992). Based on these findings, the predictions are: 12 General age differences in both types of switching costs were inspected based on pretest performance; the respective hypotheses are presented below. 91

100 Statement of Problem Prediction 1: (a) Specific switch costs will be reduced to a similar degree in younger and older adults as a function of training. (b) Given that the training in the present study was relatively intense, this reduction is also expected for children. (c) However, specific switch costs in all age groups will still be reliable after training. Prediction 2: The influence of verbal self-instructions on specific switch costs and age differences therein is an open question. However, given that no external task-cues were provided during training, so that participants had to particularly rely on internal verbal cueing in order to maintain the task sequence, performing verbal self-instructions may lead to a reduction of specific switch costs compared to the group practicing taskswitching without verbal self-instructions. Prediction 3: Specific switch costs will not be modulated by feedback indicating the value of the verbalization strategy. That is, specific switch costs in the group performing verbal self instructions during task-switching training and receiving feedback will be similar to the group that performed the same type of training without being provided feedback. Prediction 4: Variable training will result in larger specific switch costs than training involving the same tasks in each training session. Prediction 5: Training-related benefits, (i.e., the reduction of specific switch costs from the first o the last training session) should be smaller after variable training than after training involving the same tasks in each session. The influence of verbalizations and feedback on training-related benefits on the level of specific switch costs is an open question. 92

101 Statement of Problem Near Transfer of Task-Switching Training One of the main goals of this study was to investigate age differences in the near transfer of task-switching training on the level of general as well as specific switch costs. Most previous studies indicated that age differences were more pronounced for general than for specific switch costs with larger costs for children and older adults than for younger adults (e.g., Cepeda et al., 2001; Crone et al., 2004; Kray, 2006; Kray et al., in press; Kray et al., 2004; Kray & Lindenberger, 2000; Reimers & Maylor, 2005; Mayr, 2001; Verhaeghen & Cerella, 2002). However, only very few studies to date (Minear et al., 2002; see also Bherer et al., 2005) have provided evidence that task-switching training generalizes to non-trained, but structurally similar tasks (near transfer) in younger and older adults. Minear and colleagues (2002; see also Minear, 2004) found this transfer only on the level of general switch costs, and it was also more pronounced for older than for younger adults. However, in the Bherer et al. (2005) experiment, transfer was present for both types of switching costs, but there were no age differences in the amount of transfer. The aim of the present study was to replicate near transfer of task-switching training in younger and older adults and to extend this finding to childhood by comparing effects resulting from training only involving the two single tasks with effects resulting from task-switching training in children, younger, and older adults. Thus, the predictions were: Prediction 6: Age differences will be more pronounced on the level of general switch costs than on the level of specific switch costs. General switch costs will be characterized by a u-shaped age function, that is, the costs will be larger in children and older adults than in younger adults. 93

102 Statement of Problem Prediction 7: Near transfer (i.e., the reduction of general and specific switch costs from pretest to posttest) will be larger for the groups that were trained in task switching than for the groups only trained in single-task performance. Prediction 8: The reduction of general switch costs from pretest to posttest (i.e., near transfer) will be more pronounced for older adults than for younger adults. However, the amount of near transfer in children is a fully open question. Also in the focus of the present study was the question whether near transfer can be modulated by means of verbal self-instructions, feedback, and training variability. It has been demonstrated for memory or reasoning tasks that verbal self-instruction training resulted in greater performance improvements than practicing the tasks without verbal strategies (Asarnow & Meichenbaum, 1979; Derwinger et al., 2003; Kennedy & Miller, 1976; Meichenbaum & Goodman, 1971). With respect to task switching, Kray et al. (in press) showed that verbal self-instructions proved to be effective for enhancing the selection and maintenance of task sets (i.e., general switch costs), especially in children and older adults. However, the influence of the verbalizations on specific switch costs was less pronounced. In the present study, subjects will be instructed to label the upcoming task goal during the preparation interval, that is, to say aloud the alternating task sequence (cf. Kray et al., in press; Kray et al., 2004). The question is whether performance improvements associated with verbal self-instructions performed during training can be transferred to a similar switching task after training. Based on findings from Kray et al. (in press), the hypotheses for this study are: Prediction 9: The reduction of general switch costs from pretest to posttest will be larger for the groups trained in task switching and the use of verbal self-instructions than for the groups only trained in task switching without the verbal strategy. 94

103 Statement of Problem Prediction 10: This near transfer of task-switching and verbal self-instruction training may be more pronounced for children and older adults compared to younger adults. Prediction 11: For specific switch costs, there should be less or no difference in the amount of transfer between the groups that were trained in task switching and verbal self-instructions and those only trained in task switching without the verbalizations. Although the findings from Kray and colleagues (in press) point to an important function of verbal processes for action control, effects of verbal self-instruction training are not always easily transferable to other situations. Especially children fail to spontaneously use verbal strategies in new task situations (Flavell et al., 1966; Ringel & Springer, 1980; Salomon & Perkins, 1989). It has been suggested that this lack of transfer is due to subjects not evaluating the outcome of their own actions and not being aware of the training strategy s value (Flavell et al., 1966). One way to raise the children s awareness of a strategy s effectiveness is to provide explicit feedback regarding their task performance and the strategy value (cf. Dowsett & Livesey, 2000; Kennedy & Miller, 1976; Ringel & Springer, 1980). Thereby, an evaluation process is initiated and awareness of the strategy s effectiveness is increased. In sum, there is evidence suggesting that at least in children, feedback regarding the value of a verbal training strategy can promote transfer of this strategy to a similar task after memory training. Thus, participants in the present study periodically received feedback indicating the usefulness of the verbalization strategy (for details, see p. 119). The aim was to investigate whether the increase in transfer associated with feedback in other paradigms is also found after task-switching and verbal self-instruction training, that is, whether the amount of near transfer can be modulated by means of explicit feedback. 95

104 Statement of Problem Prediction 12: At least for children, the reduction of general switch costs from pretest to posttest (i.e., near transfer) may be larger for the groups receiving feedback emphasizing the value of the verbal strategy than for the groups only receiving task switching and verbal self-instruction training without feedback. This modulation of verbal self-instruction transfer by means of feedback should not be found or at least to a smaller extent for adults. Prediction 13: Given that the influence of verbal processes on near transfer with respect to specific switch costs should be limited, there probably will be no modulation of this near transfer by feedback regarding the verbal strategy. That is, the pretest-posttest reduction of specific switch costs in this group will be similar to the task-switching training group. Several studies have focused on other conditions supporting the occurrence of transfer. These experiments indicated that some types of training are known to slow down skill acquisition during training, but to be superior in terms of retention and transfer afterwards (for a review, see Rosenbaum et al., 2001; Schmidt & Bjork, 1992). Among these types of training is variable training, that is, exposing subjects to different material during practice. Sanders and colleagues (2002), for instance, have shown that high variability training in mental calculation supported transfer to non-trained tasks in young adults. Likewise, transfer of dual-task training was increased in younger and older adults when training tasks were variable (Kramer et al., 1995). The aim of this study was to investigate whether variable training also promotes the near transfer of task-switching training. Therefore, participants in the variability group were trained with different tasks and stimuli in each training session. The corresponding prediction is: 96

105 Statement of Problem Prediction 14: Near transfer of task-switching training will be larger after variable training than after training involving the same tasks in each training session, at least for adults. That is, the reduction of general switch costs from pretest to posttest will be larger in the variable training group than in the groups practicing the same tasks in each training session. Effects regarding children are an open question. Far Transfer of Task-Switching Training Aside from near transfer to a similar switching task, this study also investigated far transfer to other executive control tasks, namely the Stroop task as well as to verbal and visuospatial working memory tasks. Given that these transfer tasks share executive control demands with the switching task applied during training, such as the inhibition of currently irrelevant information and the online maintenance of task-relevant information, it seems reasonable to expect transfer effects (cf. Schmidt & Bjork, 1992). Also, far transfer to another task domain (fluid intelligence) was investigated. Prior evidence for far transfer of executive control training is very limited however, there are a few studies indicating that far transfer (of executive control training other than task switching) to other executive control tasks (Dowsett & Livesey, 2000; Fisher & Happé, 2005; Kloo & Perner, 2003; Rueda et al., 2005) and even to aspects of fluid intelligence (Klingberg et al., 2005; Klingberg et al., 2002b; Rueda et al., 2005) can be achieved in children. Evidence for far transfer of executive control training in adults is lacking, and so are findings for the modulation of far transfer by means of verbalization, feedback, and variability. It should also be noted that no prior task-switching studies have investigated far transfer of training. Thus, the aim of this study was to provide first evidence for far transfer of task-switching training to other executive tasks and to another task domain in different age groups. Given that transfer is usually less pronounced the more dissimilar the training tasks and transfer tasks are (cf. Klauer, 2001), one would also expect less modulation 97

106 Statement of Problem of these far transfer effects by the type of training (i.e., verbalizations, feedback, and variability). Since there is no prior evidence for far transfer of task-switching training, most of the following predictions are relatively unspecific. The hypotheses are structured along the different far transfer measures (i.e., the Stroop task, verbal working memory, visuospatial working memory, and fluid intelligence): Prediction 15: If task-switching training improves executive control processes, such as the ability to inhibit currently irrelevant information, far transfer (i.e., the reduction of Stroop interference from pretest to posttest) should be larger after task-switching training than after single-task training 13. Prediction 16: If task-switching training also fosters executive control processes on the level of task maintenance, then far transfer to verbal and visuospatial working memory (i.e., an increase of correctly recalled items from pretest to posttest) should be larger after task-switching training than after single-task training. Prediction 17: Also, given that especially verbal working memory is supposed to rely on internal verbal rehearsal processes, this far transfer may be larger for participants receiving additional verbal-self-instruction training. For children, this effect may only be found when feedback emphasizing the value of the verbalization strategy was provided during training. 13 This phrasing may sound confusing transfer is of course only expected after task-switching training, and not after single-task training. However, given that participants performed the tasks for the second time at posttest (the pretest being the first time), there may also be a certain performance increment at posttest simply reflecting retest effects. Therefore, transfer effects after task-switching training should exceed this retest effect. This note applies to all transfer measures. 98

107 Statement of Problem Prediction 18: Consistent with prediction 16 (verbal working memory), far transfer to visuospatial working memory (i.e., an increase of correctly recalled items from pretest to posttest) should be larger after task-switching training than after single-task training. Prediction 19: Given that executive control and intellectual abilities seem to be closely related (see p. 34), far transfer to fluid intelligence (i.e., an increase of correctly solved items from pretest to posttest) should - if at all - be larger after task-switching training than after single-task training. Finally, participants additionally performed control measures not supposed to rely on executive functioning, namely verbal speed, perceptual speed, and knowledge. There is neither empirical evidence nor theoretical reasons suggesting that executive control training should transfer to performance in these tasks. Thus, the last prediction is: Prediction 20: Assuming that task-switching training primarily enhances executive control processes, there should be no transfer of task-switching training to measures not supposed to rely on executive control. That is, there should be no difference between the task-switching and the single-task training groups with respect to the pretest-posttest improvements in perceptual speed, verbal speed, and knowledge. 99

108 Method II Empirical Part 4. Method Participants Overall, 216 participants were recruited for the study. Children and older adults were drawn off Saarland University s subject pool or recruited by means of flyers handed out in schools and at University events; younger adults were recruited by means of on-campus placards. However, two younger adults and four older adults did not complete all experimental sessions. Therefore, the final sample included 70 children, 70 younger adults, and 70 older adults (see Table 1). All participants were German native speakers in order to control the influence of language. They were paid 60 for participating in the eight sessions of the present study. For each participant, testing took approximately eight weeks (with one session per week). Demographic characteristics of the effective sample are summarized in Table 1. To indicate the representativeness of the sample, two psychometric tests were used, one from the fluid domain and one from the crystallized domain of intelligence (a description of both tests is provided in the Measures section, see p. 103). Consistent with previous studies and the two-component model of intelligence (cf. P. B. Baltes et al., 1998; P. B. Baltes et al., 1999; see also theoretical part, p. 34), there were differential age trends for both domains of intelligence. On the one hand, a reliable u-shaped age trend (t(207) = , p <.001) was found for a test of perceptual speed of processing, the Digit-Symbol Substitution Test, indicating that perceptual speed of processing improved during childhood and declined in older age, and that older adults performed better than children (all p s <.001). On the other hand, a linear age trend (t(207) = 29.35, p <.001) was observed for a test of semantic 100

110 Method fluid intelligence, and control measures (details are provided in the Measures section, see p. 103). The training phase consisted of four sessions à 45 minutes. During training, participants in each age group were assigned to one of five training conditions. Thus, all participants had to complete eight sessions, two sessions each for pretest and posttest assessment as well as four sessions of intensive training (see Table 2). Table 2: Schedule of the Present Study Pretest Session Training Session 3-6 Posttest Session Task switching and single tasks (tasks A + B) Training (e.g., task C + D) Task switching and single tasks (tasks A + B) Cognitive Battery: - Other executive tasks - Working memory - Fluid intelligence - Control measures Cognitive Battery: - Other executive tasks - Working memory - Fluid intelligence - Control measures Measures The following section provides an overview of the tasks applied at pretest, posttest, and in the four training sessions. First, the tasks of the cognitive battery will be described in detail; second, the task-switching paradigm used in this study will be presented along with the switching tasks applied at pretest and posttest and during training (see Table 2). 102

113 Method presented in red). In incongruent trials, the color words were interfering with the font color (e.g., blue presented in yellow) and in neutral trials the words were not semantically linked to colors (e.g., hat presented in green). This design allows calculating facilitation (congruent vs. neutral trials) as well as interference effects (incongruent vs. neutral trials) 14. Trials started with the presentation of the stimulus for 2000 ms or until the subject responded, followed by a response-stimulus interval of 700 ms. Participants first performed two practice blocks à 12 trials. Afterwards, they worked through 4 experimental blocks à 24 trials, yielding a total of 120 trials. All blocks consisted of an equal number of response types (red, blue, green, yellow), and stimulus types (congruent, incongruent, neutral). Number Stroop (cf. Salthouse & Meinz, 1995). In the number version, design and procedure were identical to the color version. Instead of color words, participants saw stimuli (1, 2, 3, 4, X, M, A, and H) that were presented one-, two-, three- or fourfold (e.g., 222, 44, AAAA) and their job was to decide how many stimuli were presented. In congruent trials, the number of stimuli was identical to their value (e.g., 22, 333). In incongruent trials, the number of stimuli interfered with their value (e.g., 111, 4) and in neutral trials the stimuli did not represent numerical information (e.g., AA, MMM). Reading Span (cf. Kane et al., 2004). In this task, participants had to recall letters against a background reading task. In each trial, they were presented an understandable or a nonsensical sentence, followed by a to-be-remembered letter (e.g., The glaring red rubber boat was never so in love. X [ Das knallrote Gummiboot war noch nie so verliebt. X ]) presented in a 13-point font. The English sentences from Kane et al. (2004) were substituted by German phrases that were understandable and suitable for children. Each sentence consisted of 7-14 words (M = 10.5 words). As soon as the sentence appeared, the subject read it aloud, verified aloud whether it made sense or not (it made sense half the time) and 14 Note that age differences in facilitation and interference are analyzed elsewhere (Kray, Karbach, & Kersken, in prep.). Given that the focus of the present study was on executive control, only interference effects were analyzed. 105

114 Method then read the letter. For instance, the participant had to say, The glaring red rubber boat was never so in love, no, X. When the participant read the letter, the experimenter pressed a button that cleared the screen for 500 ms, either followed by the next stimulus (i.e., sentence and a letter) or the recall cue. When the recall cue was presented, the participant recalled each letter from the preceding set in the order of their appearance. The set size ranged from two to five sentence-letter combinations per trial. The nine letters used were chosen to be phonologically distinct (B, F, H, J, L, M, Q, R, X). Letters were repeated across sets, but not within sets, and all were used approximately equally often in the task. For reasons of time, the 12-trial procedure applied by Kane and colleagues (2004) was shortened to eight trials (two trials each for the set size of 2, 3, 4, or 5 sentence-letter problems). Response sheets presented eight rows of blank spaces, with each row representing one set, and subjects wrote the letters they recalled from each set in the appropriate ordinal position. The test score was the number of correctly recalled trials. Counting Span (cf. Kane et al., 2004). In this task, participants recalled digits against a background counting task. Presented on a gray background, each display included different geometric shapes: 3-9 dark blue circles; 1, 3, 5, 7, or 9 dark blue squares; and 1-5 green circles. The number of the three different shapes was approximately balanced across displays in the task. Subjects were instructed to count the number of dark blue circles in each display and to repeat the total number after finishing counting. For instance, if three dark blue circles were presented, the participant should have said, One, two, three three. After the subject had repeated the total count, the experimenter blanked the screen for 500 ms either followed by the next display or the recall cue. When the recall cue was presented, participants repeated each total from the preceding set in order of their appearance. Digits were repeated across sets, but not within sets, and all numbers were used approximately the same number of times in the task. Consistent with the Reading-Span task, set sizes ranged from two to five displays per trial, with a total of eight trials. Response sheets presented eight rows of five blank spaces, 106

115 Method with each row representing one set, and subjects recalled the digits from each set in the appropriate ordinal position. The test score was the number of correctly recalled trials. 2-back Task 15 (cf. McElree, 2001). In this task, participants were presented a sequence of digits, one at a time, and were required to press the space bar when the digit presented on the screen was identical to the digit presented two positions back in the sequence. Subjects first performed two practice blocks à 29 trials, followed by four experimental blocks à 36 trials, resulting in a total of 202 trials. The target probability was 25 %, and the proportion of hits (i.e., correct responses to targets) and false alarms (i.e., erroneous responses to non-targets) was presented after each block. Blocks started with the presentation of the word attention [Achtung] in the middle of the white computer screen, followed by the presentation of the first stimulus for 1500 ms. The next stimulus was presented immediately thereafter. The test score was the PR score (i.e., the number of hits the number of false alarms). Symmetry Span (cf. Kane et al., 2004). Subjects were instructed to recall sequences of locations marked by red squares in a 4 x 4 matrix against a background symmetry-judgment task. In the symmetry task, participants were shown two letters and instructed to decide whether these letters were symmetrical along a vertical axis (they were half the time). After the subject gave an oral response to the letter display, the experimenter blanked the screen for 500 ms, followed by the 4 x 4 (5 cm x 5 cm) matrix with one of the 16 squares filled in red, presented for 650 ms (see Figure 8). Red square locations were never repeated in one set and each of the 16 squares appeared in red approximately equally often in the task. After the to-be-remembered matrix, either another pair of letters or the recall cue was presented. When the recall cue appeared, participants recalled the sequence of red-square locations in the previous displays in the order of their appearance. Set sizes again ranged from two to five 15 Although this task was applied at pretest and posttest, it was dropped from the analysis because especially children and older adults had severe problems to perform the task and the retest reliability was relatively low in these age groups (.16 and.25, respectively). The same was true for the correlations between the 2-back task and the remaining two verbal WM tasks (.34 and.35, respectively, see Appendix, Table 19). 107

116 Method symmetry-matrices per trial (eight trials in total). Response sheets presented eight rows of five 4 x 4 matrices, each row representing one set. Participants drew one X in each matrix corresponding to the red square in that display. The test score was the number of correctly recalled trials. It should be noted that in the Kane et al. (2004) study, an 8 x 8 matrix was presented instead of letters in the background task. In this matrix, some squares were filled in black, and participants decided whether the black-square design was symmetrical along the vertical axis. However, since pilot testing indicated that this task was way too complicated for children, the matrices in the background task were substituted with the letters. Navigation Span (cf. Kane et al., 2004). In this task, subjects recalled the paths of moving balls across the screen against a background task of counting the corners of polygons. In the background task, a polygon 16 was presented against a gray background with a red asterisk and an arrow in one corner of the polygon (see Figure 8). Participants started counting aloud the corners at the asterisk, mentally navigating in the direction of the arrow along the corners of the polygon. After navigating around the entire polygon, the subject said finish [Ende]. At this point, the experimenter pressed a key, erasing the polygon and presenting a gray box (approximately 20 cm x 20 cm) of 400 x 400 pixels that presented a ball display. Immediately after the onset of the gray box, one blue ball (1.5 cm in diameter) appeared in one of eight locations inside the box. The eight locations were either in one of the four corners, in the middle of the top or bottom row, or in the middle of the leftmost or rightmost column (see Figure 8). Within one second, the ball then traveled vertically, horizontally, or diagonally to the opposite side of the box. Paths were repeated across sets, but not within sets, and all were presented approximately equally often in the task. When the ball finished its way across the box, the experimenter presented another polygon or the recall cue. Participants recalled the sequence of ball paths in the preceding displays in the order of 16 In the original task version applied by Kane et al. (2004), letters instead of polygons were used. 108

117 Method their appearance as soon as the recall cue appeared. Consistent with the Symmetry-Span task, set sizes ranged from two to five to-be-remembered displays, with a total of eight sets. Response sheets presented eight rows of five squares, with each row corresponding to one set. Participants drew an arrow into each square in the correct order to indicate the movement of the ball in that display. Again, the test score was the number of correctly recalled trials. Figure 8: Illustration of the visuospatial working memory tasks used in this study. The boxes within each task represent single items. For illustration purposes, background task items are displayed in white boxes, to-be-recalled items in yellow boxes. The question marks depict the recall cue that followed each set for every task. The dashed lines in the navigation span display represent the direction that the circle moved in (within 1 second). Raven s Standard Progressive Matrices (Raven, 1988). Items presented a pattern of eight black-and-white figures arranged in a 3 x 3 matrix with one figure missing. Figures ranged from simple geometrical shapes to complex patterns. Participants were instructed to select one of eight figures presented below the matrix that would best complete the pattern. 109

118 Method Following three practice items, subjects had 2 x 10 minutes to complete 15 test items increasing in difficulty, resulting in a total of 30 trials. The test score was the number of correctly solved items. Figural Reasoning (cf. Lindenberger et al., 1993). This test was adapted from Lindenberger and colleagues (1993). Items in this test followed the format A is to B as C is to?. Problems were presented in a booklet, with the stimulus in the upper half and five response alternatives in the lower half (see Figure 9). Subjects indicated their response by naming the corresponding number 17. Items were presented one by one. Before the test phase, subjects received instructions and performed three practice trials. The experimenter terminated the test phase when subjects committed three consecutive errors or after they had answered all 16 items. The test score was the number of correctly solved items. Figure 9: Item from the figural analogies test. Items were presented one by one. Subjects indicated their response by naming the corresponding number. The correct response in this example was In the original version applied by Lindenberger et al. (1993), items were presented on a computer screen and participants responded manually via touch-screen. 110

119 Method Letter Series (cf. Lindenberger et al., 1993). This test was also adapted from Lindenberger et al. (1993) and consisted of 16 items, each containing five letters followed by a questions mark (e.g., a c e g i?). Items were displayed in booklets with the problem in the upper part of the page and five response alternatives (i.e., letters) in the lower part. Items followed simple rules such as +1, -1 or +2, +1. Participants indicated their response by naming the letter that logically fitted the position of the question mark. Items were presented one by one. Before the test phase, subjects received instructions and performed three practice trials. The experimenter terminated the test phase when subjects committed three consecutive errors or after they had answered all 16 items. The test score was the number of correctly solved items. Spot-a-Word Test (cf. Lehrl, 1977). Thirty-five items were presented successively on the screen, each containing one word and four pronounceable nonwords, numbered from one to five. Participants were instructed to identify the one word and to press the corresponding number on the keyboard. Items were drawn from a widely used German vocabulary test (Lehrl, 1977). Three practice items were provided prior to the test phase; testing time was limited to five minutes. The test score was the number of correctly solved items. Digit-Symbol Substitution Test (Wechsler, 1982). The paper-pencil version of this test was applied (from the Wechsler Adult Intelligence Scale; Wechsler, 1985). The test sheet displayed nine digit-symbol mappings. Below, 100 digits without the corresponding symbols were displayed. Participants were instructed to fill in as many symbols as possible within 90 s. The test score was the number of correctly added symbols. Digit-Letter Substitution Test (cf. Lindenberger et al., 1993). This test was identical to the Digit-Symbol Substitution Test (Wechsler, 1982), except that participants had to write letters instead of symbols. In contrast to Lindenberger et al. (1993), this test was applied in the same manner as the Digit-Symbol Substitution Test, that is, participants were instructed to 111

120 Method write as many letters as possible within 90 s. The test score was the number of correctly added letters. Letter Articulation (Kail, 1997). The measurement of the articulation rate was adapted from Kail (1997). Participants had to repeat aloud pairs of letters as rapidly as possible, five times. After showing the subjects the letters to be repeated, the experimenter said go! and measured the amount of time needed to say the letters. This procedure was repeated on four trials, each including a unique pair of letters (R-A; Q-H; M-F; B-I). The articulation rate was averaged across trials. Digit Articulation (Kail, 1997). Articulation rate for digits was measured similarly, using a unique pair of digits on each trial (7-4; 5-8; 1-6; 2-9). Word Articulation (Kail, 1997). The same procedure was used for the measurement of word articulation rate (dog-fish; hat-robe; book-pen; leg-hand). 18 For all three articulation measures, the dependent variable was the articulation time (ms). Measurement of Task Switching Apparatus. We used IBM-compatible computers for data collection. Stimuli were presented on a CTX 17-inch color monitor on black background and buttons located on the left- and right-hand side of an external keypad registered the responses. To control stimulus presentation and reaction time measurements, the software package ERTS (Experimental Run Time System) was used. The Switching Paradigm. In order to investigate age differences in task-switching performance, an internally cued task-switching paradigm was used (cf. Rogers & Monsell, 1995). To ensure that executive control components related to the process of switching tasks per se can be separated from those related to the process of maintaining and selecting two 18 Note that in German these were all one-syllable words [Hund-Fisch; Hut-Kleid; Buch-Stift; Bein- Hand]. 112

121 Method task sets, this paradigm was completed with a non-switch baseline (cf. Kray & Lindenberger, 2000). That is, not only mixed-task blocks, but also single-task blocks were administered. Participants were instructed to perform two simple tasks A (e.g., classifying pictures as fruit or vegetable) and B (e.g., classifying pictures as large or small). In single-task blocks, they only had to perform task A or task B. In mixed-task blocks, they were asked to switch tasks on every other trial (i.e., AABBAABB ). Since we know that participants strongly rely on internal cueing when external cues are absent (e.g., Baddeley et al., 2001; Emerson & Miyake, 2003), no external task-set cues were provided. That is, the task sequence in mixedtask blocks was predictable and subjects knew in advance that they had to switch tasks on every second trial. To maximize executive control demands, the experiment was designed to meet the following criteria: First, the stimuli were all ambiguous, that is, they represented both attributes of Task A as well as of Task B. In Figure 10, for example, the attributes fruit and large are presented simultaneously. This simultaneous activation of both stimulus attributes results in a strong interference at the stimulus level, because subjects have to be able to inhibit the stimulus information for the currently irrelevant task (e.g., the attribute large, while performing Task A) and to select the correct response button for the relevant task (i.e., the button on the right-hand side to select the fruit -category) (cf. Rogers & Monsell, 1995). Second, in half of the trials the responses of both tasks were mapped onto the same button, inducing high interference on the response level as well. For instance, the features fruit and large were both mapped onto the left response button (see Figure 10). Thus, every time participants had to switch tasks, a reconfiguration process was necessary to decouple the stimulus-response (S-R) mappings. Thus, irrelevant S-R mappings from the preceding trial had to be inhibited and those for the currently relevant task had to be activated (cf. Rogers & Monsell, 1995). 113

122 Method Computer screen with successive pictures Task A Food Task B Size fruit vegetable large small left button right button Figure 10: Task-switching scheme Operationalization of Task-Switching Abilities. As mentioned above, the task-switching paradigm used in this study allows the separation of two executive processes, namely the selection and maintenance of two task sets and the switch between two task sets. Accordingly, two measures of executive control were operationalized (cf. p. 41): (1) General switch costs were defined as the difference in performance between mixed-task blocks and single-task blocks: General switch costs = mean (mixed blocks) mean (single blocks) (2) Specific switch costs were defined as the difference in performance between switch (AB, AB) and nonswitch (AA, BB) trials within mixed-task blocks: Specific switch costs = mean (switch trials) mean (nonswitch trials) 114

123 Method It has often been argued that both types of costs are not fully independent. General switch costs (as well as all types of dual-task costs) always include the switching between tasks and specific switch costs also include the ability to maintain task sets in working memory. Even if one defines general switch costs as difference in performance between single and non-switch trials (which has sometimes been suggested by reviewers) one can still argue that non-switch trials are measured in a situation where subjects are required to switch. Nevertheless, there is evidence (via a confirmatory factor analysis) that both types of task-switching costs are indeed separable. Kray and Lindenberger (2000) have shown that a model with two latent factors provided a significantly better fit than a one-factor model. However, it should be noted that there was a substantial correlation between both factors (.50). Thus, given that it seems quite difficult to reach theoretical independence between these two components, this study aimed at a definition providing at least statistical independence. That is, general and specific switch costs are defined as two orthogonal withinsubjects contrasts. The first contrast (general switch costs) compares means of single-task trials against means in non-switch and switch trials (-2 1 1), and the second one (specific switch costs) compares means of non-switch trials against means of switch trials (0-1 1). Tasks and Stimuli. At pretest and posttest, participants were instructed to perform two simple tasks A (i.e., classifying pictures as fruit or vegetable) and B (i.e., classifying pictures as large or small). If the picture showed a fruit (food task) or was large (size task), subjects had to press the left response key with the left index finger. If the picture showed a vegetable (food task) or was small (size task), they were instructed to press the right response key with the right index finger (for details, see Figure 10). The same two response keys were used for both task sets. Stimuli consisted of 32 fruit and vegetable pictures (16 fruit and 16 vegetables) which were all presented in a larger and a smaller version, resulting in a total of 64 stimuli. All 115

124 Method pictures were presented and named prior to the experiment to make sure that participants were able to correctly identify the fruit and vegetables and assign them to the corresponding response categories (i.e., fruit, vegetable, small, large). The assignment of response labels to response keys was constant across subjects and experimental sessions (i.e., pre- and posttest). Above the external keyboard, a small instruction sign was presented showing the response assignment. Procedure In order to give participants the opportunity to get used to the assignment of response keys, each session started with two single-task practice blocks, consisting of 17 trials each. That is, the subjects only performed the food task or the size task within one block (first food, then size). Afterwards, they worked through 20 experimental blocks, eight single and twelve mixed blocks. The sequence of blocks was constant across subjects 19. Each experimental block consisted of 17 trials, yielding a total of 22 blocks x 17 trials = 374 trials. Single as well as mixed blocks consisted of an equal number of response types (left/right), tasks (food/size), and stimulus types (fruit/large, fruit/small, vegetable/large, vegetable/small). The sequence of stimuli in each block was randomly selected. Before the experiment started, visual and verbal instructions were provided. In addition, an instruction window appeared before each block, indicating which type of task (food, size, or both) had to be performed next. Participants were instructed to perform as quickly and as accurately as possible. After each block, feedback was provided showing the subjects mean reaction times (RT) and error rates. 19 The exact sequence of blocks was: Single (food) single (size) mixed mixed single (food) single (size) mixed mixed single (size) mixed mixed - single (food) mixed mixed - single (size) mixed mixed - single (food) mixed mixed. 116

125 Method Trial Procedure. Trials started with the presentation of a fixation-cross for 1400 ms, followed by the presentation of the target, and a 25 ms response-fixation-cross interval (RFI) (see Figure 11). The target remained visible on the screen until the subject responded. RFI = 25 ms Fixation Cross 1400 ms Max. RT + Figure 11: Trial procedure. RFI = Response-Fixation-Cross Interval. Session Procedure. Pretest and posttest sessions were identical with the exception that participants (or in case of children, their parents) additionally completed a consent form and a demographic questionnaire at the beginning of the first pretest session. As mentioned above, pretest and posttest included task-switching and single-task performance (with tasks A and B) as well as the cognitive test battery. An overview of the session schedule is provided in Table 4. The sequence of tasks within each session was constant across participants. Each session took about 70 minutes. In the first pretest and posttest session, respectively, participants had to perform the tasks for perceptual speed of processing, verbal working memory, visuospatial working memory, and fluid intelligence (except of the Raven SPM). A description of each task is provided in the Cognitive Battery section and in Table 3 (see p. 104). In the second pretest and posttest session, subjects performed the task switching and the single tasks, the Stroop 117

127 Method Symbol Substitution score), and fluid intelligence (Raven score) to prevent baseline differences between the training groups. The treatment for each training group is described in the following section. Training Groups Single-Task Training (Group 1). In the first group, subjects only practiced the two single tasks C and D, so that training in executive control should be low. That is, all four training sessions consisted of blocks in which either task C or task D had to be performed separately. In line with the Minear et al. (2002) study, this group served as a control condition to show that intensive training in single-task performance does not promote transfer in terms of executive control abilities. Task-Switching Training (Group 2). Since it has been shown that intensive training in executive control can be transferred to new task situations (Bherer et al., 2005; Kramer et al., 1995; Minear et al., 2002), subjects in the second group received practice in mixed-task blocks only, so that training in executive control should be intense. Thus, participants performed four sessions of intensive switching training with tasks C and D. Note that group 1 and 2 performed the identical number of tasks and trials, with the only difference being that group 1 only performed single-task blocks, while group 2 only performed mixed-task blocks. Task-Switching + Verbal Self-Instruction Training (Group 3). In the third group, subjects received the same treatment as the second group, i.e., mixed blocks with task C and D only. Given that the use of verbal self-instructions can facilitate task switching (Kray et al., in press), participants were in addition instructed to use a verbal self-instruction strategy, that is, to verbalize the upcoming task goal during task preparation (details of the procedure are described below). Task-Switching + Verbal Self-Instruction Training + Feedback (Group 4). The fourth group received the same switch + verbalization training as the third group. Since we know that 119

128 Method it can support transfer of training in children (Kennedy & Miller, 1976; Ringel & Springer, 1980; cf. Dowsett & Livesey, 2000), subjects were in addition provided explicit feedback regarding task performance and the usefulness of the verbalization strategy. Specifically, the experimenter emphasized the subject s performance improvements and attributed them to the verbalization strategy three times per training session (after the first and second third and at the end of the session) and pointed to the value of the verbal self-instructions for improving task performance. In case participants did not show any performance benefits, the experimenter repeated the task instructions and stressed the usefulness of the verbalizations to facilitate task switching. Task-Switching + Verbal Self-Instruction Training + Training Variability (Group 5). The fifth group received the same switch and verbalization training as the third group. Given that variable training can foster transfer to new task situations in adults (Kramer, Larish, et al., 1999; Rosenbaum et al., 2001; Sanders et al., 2002), training tasks varied in this group, that is, tasks and stimuli were different in each training session. Thus, in contrast to groups 1-4, participants practiced task C and D in the first training session, but task E and F in the second, G and H in the third, and I and J in the fourth training session (a detailed description of all training tasks is provided below). For practical reasons, the training groups will be referred to as single, switch, verbalization, feedback, and variability group in the results section. Tasks and Stimuli In the four training sessions, the switching paradigm was basically identical to the one used at pretest and posttest. However, participants performed different tasks including different stimuli, that is, participants in groups 1-4 had to perform tasks C and D in all four training sessions. In task C ( transportation task), they had to decide whether the pictures showed planes or cars, and in task D whether one or two planes/cars were presented 120

129 Method ( number task). If the picture showed a plane (transportation task) or if there was only one object (number task), subjects had to press the left response key with the left index finger. If the picture showed a car (transportation task) or if there were two objects (number task), they were instructed to press the right response key with the right index finger (see Table 5). Again, the same two response keys were used for both task sets. The experimenter presented verbal and visual instructions at the beginning of each session. Stimuli consisted of 32 plane and car pictures (16 planes and 16 cars) that were all presented in a version with just one or with two vehicles visible on the screen, resulting in a total of 64 stimuli. To prevent effects of associative learning on task-switching costs (Kray & Eppinger, 2006; see also Waszak et al., 2003), a new set of pictures was presented in every training session, resulting in a total of 4 sessions x 64 stimuli = 256 pictures. All stimuli were presented prior to the experiment to make sure that participants were able to correctly assign them to corresponding response categories. The assignment of answers to response keys was constant across subjects and experimental sessions (i.e., training sessions 1-4). Above the external keyboard, a small instruction sign was presented showing the response assignment. As described above, group 5 (switch + verbalization + training variability) was trained with different tasks and stimuli in each training session (see Table 5). The design of the tasks was identical to those used in training groups 1-4 (see above). Participants from group 5 also performed task C (transportation task) and D (number task) in the first training session, but task E (hobby task) and F (stoplight task) in the second training session. In the hobby task, participants had to classify hobby-related items as sports or music articles, and in the stoplight task, they had to decide whether the pictures were red or green. In the third training session, task G (animal task; classifying pictures as fish or bird) and H (direction task; classifying pictures as normal or rotated) were introduced. In training session four, subjects worked with 121

131 Method Session Procedure. In all five training groups, participants started with two practice blocks à 17 trials 20 followed by 24 experimental blocks à 17 trials, resulting in a total of 17 trials x 26 blocks x 4 sessions = 1768 trials of practice. That is, all five training groups performed the same number of trials during training. After each block, a feedback window on the screen indicated the subject s mean RT and proportion of errors in the previous block. All groups were offered a short break after completing the first half of the experiment. Other than that, the session procedure varied as a function of training group. Group 1 (single-task training) started each session with two single-task practice blocks (one with task D and one with task D), followed by 24 alternating experimental single-task blocks with tasks C and D. Group 2 (switch training) started each session with two mixed-task training blocks, followed by the 24 experimental mixed-task blocks. Again, it should be noted that the paradigm applied during training was identical to the one at pretest and posttest, that is, no external task cues were provided and tasks had to be switched every other trial (i.e., CCDDCCDD ). Blocks always started with task C. This procedure was identical for group 3 (switch + verbal selfinstruction training). However, participants were in addition instructed to perform a verbal selfinstruction, that is, to verbalize the next task goal during task preparation (cf. Kray et al., in press). Specifically, subjects had to say aloud transportation [Fahrzeug] or number [Anzahl] as soon as the fixation-cross appeared in each trial, depending on what task had to be performed next. In case participants stopped verbalizing during one of the blocks, they were reminded to continue using the verbal self-instruction after completing that given block. For group 4 (switch + verbal self-instruction training + feedback), the experimenter additionally provided feedback after blocks 8, 16, and 24 (see above). The session procedure for group 5 20 The short sequence of 17 trials per block was chosen for the following reason: Participants had to internally maintain the task sequence (AABBAABB ) throughout blocks. However, if they failed to switch tasks at the appropriate point in time, they lost this sequence, so that the remaining trials in this block were erroneous. In order to prevent large error rates possibly resulting from one failed task switch, a small number of trials per block was chosen, so that participants had the opportunity to start over in the following block. 123

132 Method (switch + verbal self-instruction training + variability) was identical to group 3, except that the experimenter introduced new training tasks, stimuli, and task goals to be verbalized at the beginning of each session. Data Analysis Task-Switching and Stroop-Task Data Analyses of the task-switching and Stroop-task data were restricted to mean reaction times (RT) for correct responses 21 and proportion of errors. Practice blocks and the first trial in each block were not analyzed. In order to examine age differences as well as training and transfer effects in general and specific switch costs, two orthogonal contrasts were specified: In the first one, performance in single-task blocks was compared to performance in mixed-task blocks (general switch costs). The second contrast compared performance on nonswitch trials to performance on switch trials within mixed-task blocks (specific switch costs). In order to examine age differences, two contrasts were defined for the factor Age Group: Given that quadratic age trends have been reported for different aspects of executive control, a quadratic contrast was specified as well as a contrast comparing children with adults. For the analysis of condition-specific age differences in task-switching and in the Stroop task, raw latencies can be problematic. Children and older adults tend to respond slower than younger adults regardless of the task demands or conditions. When it comes to older age, the general slowing hypothesis suggests that age effects in response time tasks 21 For task switching, latencies slower than 4000 ms were excluded from the analysis (Pretest and posttest: Children: 2.30%; younger adults: 0.09%; older adults 0.86%. Training: Children: 1.33%; younger adults: 0.01%; older adults 1.13%). 124

133 Method can be represented by a proportional factor (Brinley, 1965; Cerella, 1985; Salthouse, 1985). Thus, age by condition interactions could be confounded with age differences in baseline conditions. Therefore, age effects based on difference scores (such as switch costs or interference effects) with respect to raw latencies could simply reflect developmental increases or age-related slowing rather than condition-specific effects. A number of methods have been proposed to deal with this methodological problem (Brinley, 1965; Cerella, 1990; Chapman, Chapman, Curran, & Miller, 1994; Kliegl et al., 1994; Salthouse, 1988). The present study used log-transformed latencies to control for age-related differences in baseline performance. Compared to the analysis of raw latencies, this procedure has several additional benefits: First, children and older adults often show larger variability in performance than younger adults. Thus, the assumption of homogeneous variances between groups is often violated. This mostly can be avoided when analyses are based on log-transformed reaction times. Second, switch costs and interference costs are calculated as differences between logarithms, that is, they are equivalent to ratio scores. As a consequence, age by condition interactions are relatively independent of age differences in baseline conditions (cf. Meiran, 1996). This fact is of particular relevance when baseline differences are large, which is often the case in research on lifespan cognitive development. However, every analysis was also conducted for raw latencies and any significant differences are reported. Cognitive Battery For the verbal and visuospatial working memory (WM) tasks and for fluid intelligence, analyses were based on accuracy (% correct). To account for differences in baseline performance, the pretest-posttest difference in performance was measured relative to baseline performance at pretest. Unless reported otherwise, results of this analysis were consistent 125

134 Method with those based on the pretest-posttest comparison not corrected for differences in baseline performance. Finally, analyses for the control measures were based on the number of correctly solved items (perceptual speed, knowledge) and RT (verbal speed). Again, the pretestposttest difference in performance was measured relative to baseline performance at pretest in order to account for differences in baseline performance. The overall level of significance applied to all analyses in the present study was 5 %. To examine how significant and how broad transfer in this study was, Cohen s (1977) d, or the standardized mean difference in performance between pretest and posttest was calculated (cf. Verhaeghen et al., 1992). That is, the pretest-posttest difference (for each training and age group) was divided by the pooled standard deviation for both test occasions. All d-values were then corrected for small sample bias using the Hedges and Olkin (1985) correction factor (d ). For instance, a pretest-posttest effect size d = 1 indicates that the mean difference between pretest and posttest corresponds to one standard deviation. Missing Values Missing values were generally rare in this study. However, for technical reasons training data for two children (fourth training session) and one older adult (third training session) in the single-task training group were lost. These data were replaced with those from the preceding sessions. Also, training data from the first training session of one younger adult in the variability group were lost, which were replaced by those from the second training session. Unfortunately, training data for two children (feedback and variability group) and one older adult (feedback group) were completely lost, so that the analysis of training data was restricted to 207 instead of 210 subjects. 126

135 Results 5. Results This chapter is divided into five parts. The first part focuses on the matching procedure used to assign participants in each age group to one of the five training conditions, and the second part describes results for the training data collected in training sessions 1-4. The third part presents results for age-related changes in near transfer of task-switching training to a similar switching task, and the fourth one deals with findings regarding age-related changes in far transfer of training to other executive tasks and other task domains, and includes the analysis of the control variables. Part five is focused on the evaluation of the training program, comprising the inspection of transfer effect sizes, transfer range, proportion of transfer as well as the prediction of transfer effects. Within each section, the presentation of results is structured along the research hypotheses presented in chapter 3 (see p. 87). Matching of the Training Groups In order to control for differences in baseline performance between the training groups, participants in each age group were matched to the training conditions based on pretest performance in speed of task execution (single-task RT), general switch costs, perceptual speed (Digit-Symbol Substitution score), and fluid intelligence (Raven score). Control analyses were performed for each of these criteria to make sure the matching procedure was successful (see Table 6). Data were submitted to a two-way ANOVA with the betweensubjects factors Age Group (children, younger adults, older adults) and Training Group (single, switch, verbalization, feedback, variability). Speed of Task Execution. Analysis of the single-task RT revealed a quadratic age effect, F(1, 195) = , p <.0001, ŋ 2 =.47, indicating that children and older adults 127

136 Results responded slower than younger adults. Also, children showed longer latencies than older adults, F(1, 195) = 42.58, p <.0001, ŋ 2 =.09. Neither the main effect for training group (p =.41) nor the interaction with age group reached significance (p =.97). General Switch Costs. Results for general switch costs revealed a quadratic age effect, F(1, 195) = 24.32, p <.0001, ŋ 2 =.01, with larger costs for children and older adults than for younger adults, but no difference between children and older adults (p =.11). There was neither a significant effect for training group (p =.77) nor an age group training group interaction (p =.99). Perceptual Speed. Results for the Digit-Symbol Substitution Test scores showed a significant quadratic age effect, indicating that younger adults performed better than children and older adults, and older adults also outperformed children (all p s <.001). Neither the main effect for training group (p =.80) nor the interaction between age group and training group was significant (p =.98). Fluid Intelligence. For Raven scores, there also was a significant quadratic age effect showing better performance in younger adults than in children and older adults, F(1, 195) = 27.85, p <.0001, ŋ 2 =.02, without a difference between children and older adults (p =.92). Neither the main effect for training group (p =.31) nor the interaction between age group and training group was reliable (p =.70). In sum, none of the control analyses performed for the four matching criteria revealed any baseline differences between the training groups at pretest, indicating that the matching procedure was successful. Thus, there are no pre-training differences between the training groups that could render the interpretation of training and transfer effects more complicated. In addition, it should be noted that all further analyses are based on log-transformed latencies, so that possible inter-group differences are accounted for (for details, see Method, p. 124). 128

138 Results Training Data Before transfer effects were analyzed, data from the four training sessions were inspected. Although the main focus of the present study is on transfer rather than training effects, the results of the training data form an important basis for the interpretation of the subsequent transfer results. Thus, in order to examine the influence of extensive practice on single-task and task-switching performance, two sets of analyses were performed. The first one focused on the single-task training group (group 1) and the second one on the groups that were trained in task switching (groups 2-5). Data for group 1 were subjected to a two-way ANOVA with the between-subjects factor Age Group (children, younger adults, older adults) and the within-subjects factor Session (training 1, 2, 3, 4,). Data for groups 2-5 were subjected to a four-way ANOVA including the additional between-subjects factor Training Group (switch, verbalization, feedback, variability) and the within-subjects factor Trial Type (nonswitch, switch). Means and error rates for all experimental conditions are provided in the Appendix (Table 14 16). Single-Task Training (Group 1) Latencies. Analysis of the single-task training data revealed a quadratic age effect, F(1, 39) = 33.74, p <.0001, ŋ 2 =.36, with slower latencies for children and older adults than for younger adults, and slower responses for children than for older adults, F(1, 39) = 20.80, p <.0001, ŋ 2 =.22. In addition, there was a significant main effect for session 22, F(3, 117) = 5.51, p <.01, ŋ 2 =.13, revealing a general reduction of latencies from the first to the last training session, F(1, 39) = 7.25, p =.01, ŋ 2 =.15 (see Figure 12, left panel). 22 Based on mean RT, the main effect for session was not significant. 130

139 Results Children Younger adults Older adults Single Task RT (ms) Error rates (%) Training 1 Training 2 Training 3 Training 4 0 Training 1 Training 2 Training 3 Training 4 Figure 12: Mean RT (ms, left panel) and accuracy (error rates, right panel) for the single-task training group as a function of age group (children, younger adults, older adults) and session (training 1, 2, 3, 4). Error bars refer to standard errors of the mean. Accuracy. The analysis on the level of error rates showed a main effect for age group, F(2, 39) = 10.82, p <.001, ŋ 2 =.36, indicating that error rates were generally higher in children than in adults, F(1, 153) = 21.04, p <.0001, ŋ 2 =.35, but that there was no difference between younger and older adults (p =.44). The main effect for session, F(3, 117) = 3.64, p <.05, ŋ 2 =.07, interacted with age group, F(6, 117) = 3.13, p <.01, ŋ 2 =.13. Post-hoc comparisons showed that in contrast to older adults, error rates in children and younger adults generally increased from the first to the last session, F(1, 13) = 8.94, p =.01, ŋ 2 =.41, and F(1, 13) = 6.97, p <.05, ŋ 2 =.34 (see Figure 12, right panel). Task-Switching Training (Groups 2-5) (Predictions 1-5) Results of the overall ANOVA on the level of latencies are presented in Table 7. All main effects were significant, and so were a number of the two-way interactions as well as a three-way interaction between session, training group, and trial type. The overall results for error rates were generally consistent with those based on latencies. Except for the factor 131

141 Results In order to inspect the pattern of age differences regarding training-related benefits within each of the training conditions, separate analyses were performed for these groups. Since there were no specific expectations regarding the course of practice effects across the four sessions of training, the following analyses are focused on the comparison between sessions one and four. However, in order to disentangle the significant interactions with the factor Training Group, a follow-up ANOVA including all four training groups and the training sessions one and four was performed. Of special importance for the interpretation of the subsequent transfer effects is the significant three-way interaction between session, trial type, and training group, because it allows determining whether the type of task-switching training modulated the amount of training related benefits (i.e., the reduction of specific switch costs from the beginning to the end of training). First, the results for each of the training groups are presented separately, followed by the most important analysis, namely the disentanglement of the interactions with the factor Training Group. Results for latencies in each training group revealed a quadratic age effect, showing slower RT in children and older adults than in younger adults, and also faster RT in older adults than in children (all p s <.05; see Figure 13). In addition, specific switch costs were reliable in all training groups, both on the level of latencies and accuracy (all p s <.001). With respect to latencies, a quadratic age function was found for specific switch costs under all training conditions, that is, costs were larger for children and older adults than for younger adults (all p s <.05). Given that these effects were consistent across training conditions and also in line with the overall analysis, they will not be reported separately for each training group. Instead, the focus will be on interactions with the factor Session and only significant effects will be reported. 133

143 Results Task-Switching Training (Group 2) Latencies. RT was generally reduced from the first to the fourth training session, F(1, 39) = 52.67, p <.0001, ŋ 2 =.60. A session trial type interaction, F(1, 39) = 56.08, p <.0001, ŋ 2 =.75, indicated that specific switch costs were also reduced from the first to the fourth training session (see Figure 13). This reduction was not modulated by age group (p =.82). Accuracy. Results based on error rates showed that children generally committed more response errors than adults, F(1, 39) = 5.45, p <.05, ŋ 2 =.12, without a reliable difference between younger and older adults (p =.16). No interactions with session were found. Task-Switching and Verbal Self-Instruction Training (Group 3) Latencies. RT was reduced from the first to the last session, F(1, 39) = 82.89, p <.0001, ŋ 2 =.64, and this reduction was larger for children than for adults, F(1, 39) = 6.07, p <.05, ŋ 2 =.05, but equal for younger and older adults (p =.38). An interaction between session and trial type, F(1, 39) = 45.10, p <.0001, ŋ 2 =.54, showed that specific switch costs were generally reduced from training session one to four (see Figure 13). The interaction with age group was not significant (p =.50). Accuracy. Again, error rates were larger for children than for adults, F(1, 39) = 18.41, p =.0001, ŋ 2 =.32, but there was no difference between younger and older adults (p =.57). In contrast to older adults, children and younger adults made more response errors in the last training session than in the first one, F(1, 39) = 27.82, p <.0001, ŋ 2 =.39, and F(1, 39) = 17.73, p =.0001, ŋ 2 =.25. Task Switching, Verbal Self-Instructions, Feedback (Group 4) Latencies. RT was reduced from the first to the last session, F(1, 37) = 74.40, p <.0001, ŋ 2 =.65. Consistent with the previous groups, specific switch costs were reduced from 135

144 Results the first to the last training session, F(1, 37) = 52.38, p <.0001, ŋ 2 =.56, but this reduction was not modulated by age group (p =.84). Accuracy. The ANOVA based on error rates also revealed that children made more response errors than adults, and that younger adults committed more errors than older adults, F(1, 37) = 14.07, p <.001, ŋ 2 =.13, and F(1, 37) = 7.28, p <.05, ŋ 2 =.24, respectively. Task Switching, Verbal Self-Instructions, Variability (Group 5) Latencies. Analysis for the variability group also showed a general reduction of RT from the first to the last training session, F(1, 38) = 74.85, p <.0001, ŋ 2 =.62, that was more pronounced in children than in adults, F(1, 38) = 7.75, p <.01, ŋ 2 =.06, but equal for younger and older adults (p =.36). Furthermore, specific switch costs were generally reduced from the first to the last training session, F(1, 38) = 16.09, p <.001, ŋ 2 =.25 (see Figure 13). However, there were no age differences in the reduction of specific switch costs (p =.28) 24. Accuracy. Analysis on the level of error rates showed that children committed more response errors than adults, F(1, 38) = 13.77, p <.001, ŋ 2 =.25, without a difference between younger and older adults (p =.13). In contrast to adults, children made more errors in the last session than in the first one, F(1, 38) = 10.43, p <.01, ŋ 2 =.20, but there was no difference between younger and older adults (p =.10). Finally, and most importantly, results for the follow-up analysis disentangling the interactions with the factor Training Group are reported. In this analysis, data were subjected to a four-way ANOVA with the between-subjects factor Age Group (children, younger adults, older adults) and Training Group (switch, verbalization, feedback, variability), and the within- 24 Based on mean RT, this reduction was larger for children than for adults, F(1, 38) = 14.00, p <.001, ŋ 2 =

145 Results subjects factors Session (training 1 and 4) and Trial Type (nonswitch switch). However, the focus was on interactions with the factor Training Group. Latencies. The analysis based on latencies showed a trial type training group interaction, F(3, 153) = 3.68, p =.01, ŋ 2 =.01, indicating that specific switch costs were smaller in the groups performing verbal self instructions during training (groups 3-5) than in the task-switching training group without verbalizations, F(1, 153) = 8.95, p <.01, ŋ 2 =.01, but that there was no difference between the verbalizing groups (all p s >.16). This interaction was not modulated by age group (p =.15). Importantly, the interaction between session and trial type 25 was qualified by training group, indicating that the reduction of specific switch costs from the first to the last training session was smaller for the variability group (group 5) than for the remaining training groups, F(1, 153) = 12.18, p <.001, ŋ 2 =.03, and that there was no difference between the groups 2 4 (all p s >.81). Note that this difference between the variability group and the remaining training groups was not modulated by age group (p =.63). Accuracy. On the level of accuracy, none of the interactions with the factor Training Group reached significance (all p s >.18). Summary. Thus, to sum up the results for the training phase, analyses for the singletask training group showed that latencies were reduced as function of practice in all age groups; however, in contrast to older adults, children and younger adults showed an increase in error rates at the same time. When it comes to the task-switching training groups, effects of age group and training group were most pronounced on the level of latencies: Specific switch costs were characterized by a quadratic age function, indicating that costs were larger for children and 25 On the level of mean RT, the interaction between session, trial type, and age group also reached significance, F(2, 153) = 8.38, p <.0001, ŋ 2 =.05, showing that the reduction of specific switch costs as a function of training was larger for children than for adults, F(1, 153) = 16.51, p <.0001, ŋ 2 =.05, without a difference between younger and older adults (p =.64). 137

146 Results older adults than for younger adults (cf. Kray et al., in press). Furthermore, specific switch costs were reduced when verbal self-instructions were performed (confirming prediction 2), while neither the additional feedback (confirming prediction 3) nor the variable training tasks (disproving prediction 4) modulated specific switch costs. Most importantly, specific switch costs were reduced - but not eliminated - as a function of training in all age groups (cf. Bherer et al., 2005; Kray & Lindenberger, 2000; Kramer, Hahn, et al., 1999) and this reduction occurred to a similar degree in all age groups (confirming predictions 1a-1c). However, while this reduction was less pronounced in the variability group (confirming prediction 5), there neither was an influence of verbal selfinstructions nor of feedback. It should be noted, however, that the variability group is not completely comparable with the remaining training groups, because the results may be confounded with differences in task difficulty. Effects on the level of accuracy were less pronounced and generally in line with those based on latencies. However, it should be noted that similar to the single-task training group, children and sometimes also younger adults showed an increase of error rates from the first to the last training session, pointing to a speed-accuracy trade-off. Importantly, this decrease of accuracy was only found on the level of absolute error rates, but not on the level of switch costs. 138

147 Results Age-Related Differences in the Near Transfer of Task-Switching Training to a Similar Switching Task This section is divided into two parts: The first part focuses on task switching at pretest in order to make sure that the general pattern of age-related differences in performance found in this study was consistent with previous findings. The second part deals with near transfer of task-switching training to a similar switching task. Analysis of Pretest Task-Switching Data (Prediction 6) In order to examine age-related differences in task-switching abilities at pretest, data were subjected to a two-way ANOVA with the between-subjects factor Age Group (children, younger adults, older adults) and the within-subjects factor Trial Type (single, nonswitch, switch). Results for mean RT and accuracy are shown in Figure 14 (right and left panel). In order to demonstrate the effects of log-transformation as a means to account for age differences in baseline performance, results based on log-transformed latencies are exemplarily shown in addition (see Figure 14, middle panel). Latencies. Analysis revealed a significant quadratic age effect, indicating that younger adults responded faster than older adults and children, F(1, 195) = , p <.0001, ŋ 2 =.48. Post-hoc comparisons showed that children also responded slower than older adults, F(1, 195) = 23.71, p <.0001, ŋ 2 =.05. In addition, there was a main effect for trial type, F(2, 390) = , p <.0001, ŋ 2 =.82, with significant general and specific switch costs, F(1, 195) = , p <.0001, ŋ 2 =.82, and F(1, 195) = , p <.0001, ŋ 2 =.83. In line with previous findings, there was a quadratic age function for general switch costs, indicating that they were larger for children and older adults than for younger adults, F(1, 195) = 24.32, p <.0001, ŋ 2 = 139

148 Results.02, but that there was no difference between children and older adults (p =.11). However, no age differences were found for specific switch costs 26. Table 8: Mean RT (ms) and Standard Deviations (SD) at Pretest as a Function of Age Group (Children, Younger Adults, Older Adults) and Trial Type (Single, Nonswitch, Switch); General Switch Costs and Specific Switch Costs as a Function of Age Group (Children, Younger Adults, Older Adults) Trial type Switch costs Age group Single Nonswitch Switch General Specific M SD M SD M SD M SD M SD Children Younger adults Older adults Accuracy. Analysis based on error rates showed that children committed more response errors than adults, F(1, 195) = 29.54, p <.0001, ŋ 2 =.12, but that the difference between younger and older adults failed to reach significance (p =.08). In line with the latencies, there was a significant effect for trial type, F(2, 390) = 68.16, p <.0001, ŋ 2 =.24, yielding general switch costs as well as specific switch costs, F(1, 195) = 69.02, p <.0001, ŋ 2 =.23, and F(1, 195) = 66.48, p <.0001, ŋ 2 =.24. General switch costs were also larger for children than for adults, F(1, 195) = 14.93, p <.0001, ŋ 2 =.05, but there was no difference between younger and older adults (p =.16). Consistent with the analysis based on RT, no age differences were found for specific switch costs (p =.21). 26 The analysis based on mean RT also revealed a quadratic age effect for specific switch costs, F(1, 195) = 31.53, p <.0001, ŋ 2 =.04, that disappeared for log-transformed RT (see Figure 14). 140

149 Results Mean RT (ms) Log-Transformed RT (ms) 0,45 0,40 0,35 0,30 0,25 0,20 0,00 Children Young Adults Older Adults Children Young Adults Older Adults Error Rates (%) General Switch Costs Specific Switch Costs Children Young Adults Older Adults Figure 14: General and specific switch costs at pretest based on mean RT (left panel), log-transformed RT (middle panel), and error rates (right panel) as a function of age group (children, younger adults, older adults). Error bars refer to standard errors of the mean. Summary. Thus, in line with previous studies, results for mean latencies and accuracy showed reliable general switch costs and specific switch costs. Age differences for general switch costs followed a quadratic developmental function (cf. Cepeda et al., 2001; Kray et al., in press; Kray et al., 2004; Reimers & Maylor, 2005), but there were no age differences for specific switch costs when age differences in baseline performance were accounted for by means of log-transformation (cf. Kray et al., 2004; Reimers & Maylor, 2005; see also Figure 14) (confirming prediction 6). Near Transfer of Task-Switching Training: The Influence of Verbal Self-Instructions, Feedback, and Training Variability (Predictions 7-14) In order to examine near transfer to a similar switching task, pretest and posttest data were submitted to a four-way ANOVA with the between-subjects factors Age Group (children, younger adults, older adults) and Training Group (single, switch, verbalization, feedback, variability), and the within-subjects factors Session (pretest, posttest) and Trial Type (single, nonswitch, switch). Mean RT and error rates for all experimental conditions are provided in the 141

150 Results Appendix (Table 17 and Table 18). Given that the pattern of age differences in the speed of responding as well as in general and specific switch costs was completely consistent with the pretest data, the effects are not presented again. Instead, the focus is on interactions with the factors Session and Training Group. Latencies. ANOVA based on latencies revealed a main effect for session, showing that RT decreased from pretest to posttest, F(1, 195) = , p <.0001, ŋ 2 =.68. Session interacted with age group, F(2, 195) = 4.06, p <.05, ŋ 2 =.01, showing that this decrement was more pronounced for children and younger adults than for older adults (both p s =.01), and with trial type, F(2, 390) = , p <.0001, ŋ 2 =.48, showing that both types of switch costs decreased from pretest to posttest, general switch costs: F(1, 195) = , p <.0001, ŋ 2 =.52, and specific switch costs: F(1, 195) = , p <.0001, ŋ 2 = However, most important for the present study were interactions between the factor Training Group and other experimental factors. While the main effect for training group was not significant (p =.34), the analysis showed interactions between session training group, F(4, 195) = 5.36, p <.001, ŋ 2 =.03, session training group trial type, F(8, 390) = 7.00, p <.0001, ŋ 2 =.06, and session training group trial type age group, F(16, 390) = 2.19, p <.01, ŋ 2 =.04. In order to disentangle these interactions, several contrasts were specified for the factor Training Group: Comparing groups 1 and 2 showed that the reduction of general switch costs from pretest to posttest was larger after task-switching training (group 2) than after single-task training (group 1), F(1,156) = 25.11, p <.0001, ŋ 2 =.05, and that this transfer effect was more pronounced for children and older adults than for younger adults, F(1,156) = 4.57, p 27 Analysis based on mean RT also revealed a session trial type age group interaction, F(4, 390) = 6.22, p <.0001, ŋ 2 =.02, with a quadratic age function for the reduction of general switch costs from pretest to posttest, F(2, 195) = 8.71, p <.001 ŋ 2 =.02. That is, children and older adults showed a larger reduction of general switch costs from pretest to posttest than younger adults. 142

151 Results <.05, ŋ 2 =.03 (see Figure 15) 28. That is, based on log-transformed RT, children reduced their general switch costs by 62%, younger adults by 41%, and older adults by 56%. Comparisons of group 2 and 3 (p =.96) and group 3 and 4 (p =.59) indicated that near transfer was neither modulated by verbal self-instructions performed during task-switching training nor by additional feedback indicating the value of the verbalization strategy. Finally, a comparison between group 2/3/4 29 and 5 revealed that transfer (i.e., the reduction of general switch costs from pretest to posttest) was reduced in children and increased in adults when training tasks were variable 30, F(1, 65) = 9.17, p <.01, ŋ 2 =.07, and F(1, 130) = 8.88, p <.01, ŋ 2 =.02 (see Figure 15). With respect to specific switch costs, the reduction from pretest to posttest was also larger after task-switching training (group 2) than after single-task training (group 1), F(1,156) = 6.27, p =.01, ŋ 2 = 01, but this near transfer was neither modulated by verbalizations, feedback, and variability (all p s >.20), nor by age (p =.24). 28 Aside from the log-transformation as a means of controlling for age differences in baseline performance, hierarchical regression analysis served to examine whether age differences in the amount of transfer in the switch group (group 2) were still present after removing the effects of age-related changes in general switch costs at pretest. Using reduction of general switch costs as the dependent variable, general switch costs at pretest were first entered into the regression procedure, followed by age, and the quadratic function of age. Although age added a significant amount of unique variance, R 2 =.05, F (1, 39) = 4.57, p <.05, the quadratic function of age did not (p =.30). 29 Given that there was no difference between groups 2, 3, and 4, data were collapsed across these groups to increase statistical power. However, the same pattern was found when group 2 was compared separately to group 5 (all p s <.05). 30 Again, hierarchical regression analysis was used as a second method to examine whether the age differences in the amount of transfer in the variability group (group 5) were still present after removing the age-related changes in general switch costs at pretest. General costs at pretest were entered first into the model, followed by age, and a square root transformed function of age. Both age and the square root age function added a significant amount of unique variance, R 2 =.11, F (1, 39) = 10.10, p <.01, and R 2 =.09, F (1, 39) = 9.80, p <

153 Results Accuracy. Analysis based on error rates yielded a main effect for session, F(1, 195) = 6.95, p <.01, ŋ 2 =.03, that was modulated by age group, F(2, 195) = 8.70, p <.001, ŋ 2 =.07, indicating that children and younger adults committed more errors at posttest than at pretest, while older adults showed a reduction of error rates at posttest, F(1, 195) = 17.37, p <.0001, ŋ 2 =.07. Also, there was a session training group interaction, F(4, 195) = 3.40, p =.01, ŋ 2 =.06. Post-hoc comparisons showed that this interaction was caused by the feedback group (group 4) committing more errors at posttest than the remaining groups, F(1, 195) = 9.85, p <.01, ŋ 2 =.04. No further interactions reached significance. Summary. Results clearly showed that the reduction of general switch costs from pretest to posttest was larger after task-switching training than after single-task training (confirming prediction 7), that is, there was near transfer from task-switching training to a similar switching task, especially in children and adults (confirming prediction 8). This near transfer was not modulated by verbal self-instructions (disproving predictions 9 and 10) and feedback (disproving prediction 12). Interestingly, it was reduced in children and increased in adults when the training tasks were variable (confirming prediction 14). In addition, the taskswitching training also resulted in near transfer on the level of specific switch costs (confirming prediction 7), but there were no modulations by age group or by the type of training (confirming predictions 11 and 13). Transfer effects were restricted to the level of RT. 145

154 Results Far Transfer of Training to Other Executive Tasks and Other Task Domains Aside from near transfer to a similar switching task, the aim of this study also was to assess far transfer to other executive abilities (inhibitory control, WM) and to another task domain (fluid intelligence). Accordingly, this section is divided into three parts. The first part focuses on the structure of the cognitive abilities assessed with the tasks in the cognitive test battery (see p. 103). Given that two or three indicators were used to measure each construct in order to increase the reliability and validity of measurement, the first step was to examine whether data for each construct can be collapsed across tasks for the analysis of far transfer effects. The second part analyzes far transfer of task-switching training to other executive tasks, namely to the Stroop task, to verbal and visuospatial WM tasks as well as to another task domain, that is, fluid intelligence. In the third part, the control measures are investigated. Before transfer effects were analyzed for each of the transfer domains, pretest performance was inspected to make sure that the pattern of age-related differences found for each domain was consistent with previous studies. Structure of Cognitive Abilities In order to examine far transfer of task-switching training to other executive tasks and other task domains, a total of 16 tests was used in the cognitive test battery to investigate seven domains of cognitive abilities: Inhibitory control (Color Stroop, Number Stroop), verbal working memory (Reading Span, Counting Span), visuospatial working memory (Symmetry Span, Navigation Span), fluid intelligence (Raven s Standard Progressive Matrices, Figural Reasoning, Letter Series), knowledge (Spot-a-Word Test), perceptual speed (Digit-Symbol Substitution Test, Digit-Letter Substitution Test), and verbal speed (Letter Articulation Rate, Digit Articulation Rate, Word Articulation Rate) (see Table 3). In order to investigate whether the tasks are reliable indicators for the respective constructs, the ability structure was tested 146

155 Results by means of confirmatory factor analysis (CFA) using structural equation modeling (with the software package AMOS, Arbuckle, 2005). The advantage of this approach is that intercorrelations between constructs at the latent level are corrected for measurement errors (Nachtigall, Kroehne, Funke, & Steyer, 2003). Prior to model fitting, the raw data were checked to examine whether they were consistent with the assumption of multivariate normality. Kurtosis estimates did not exceed 1 or -1 for any of the measures 31, suggesting that the distributional properties of the data warranted the use of standard maximum likelihood chi-square estimation procedures. Analyses were based on z-transformed data, and model fitting on the variancecovariance matrix. A chi-square test compared the observed covariance matrix with the model covariance matrix. Given that the model represents the null hypothesis, non-significant results indicate that the model does not differ from the empirical data. Given that the power of the chisquare test extremely depends on the sample size, it has been suggested to rely on the chisquare/degrees of freedom ratio instead of the chi-square p-value. This index should be smaller than two (Schermelleh-Engel, Mossbrugger, & Müller, 2003). In addition, the Comparative Fit Index (CFI), the Non-Normed Fit Index (NNFI), and the Root Mean Square Error of Approximation (RMSEA) will be reported as indexes of incremental fit (Bentler, 1989; Hu & Bentler, 1999). According to Bentler (1989), values larger than.90 are desirable for the CFI and the NNFI. In samples including less than 250 subjects, the RMSEA should be smaller than.08 (Hu & Bentler, 1999). Also, chi-square values, degrees of freedom, and corresponding p-values for all models are reported (cf. Raykov, Tomer, & Nesselroade, 1991). Table 9 provides an overview of the model-fitting procedure. In a first step, a six first-order factor model structure with the factors inhibitory control, verbal WM, visuospatial WM, fluid intelligence, perceptual speed, and verbal speed was 31 The only exception was Figural Reasoning (4.67). 147

156 Results specified 32 (M1). Unfortunately, this model was not admissible, indicating that the specified model structure did not fit the data. The inspection of the correlation matrix showed that the correlations between the respective indicators for each construct were high, that is,.51 for verbal WM,.57 for visuospatial WM, for fluid intelligence,.92 for perceptual speed, and for verbal speed. However, when it comes to inhibitory control, the correlation between the interference effects for the Color Stroop version and the Number Stroop version was not significant (-.10). The complete correlation matrix is provided in the Appendix (Table 19). Therefore, in the second step, the initial model was modified and a five first-order factors model (M2) with the factors verbal WM, visuospatial WM, fluid intelligence, perceptual speed, and verbal speed was fit to the data (see Figure 16). Inhibitory control was dropped from the model, because the inspection of the correlations suggested that both indicators (Color Stroop interference and Number Stroop interference) could not be loaded on the same factor. This modified model showed satisfactory fit indexes for the whole sample including all three age groups (M2) and also for each of the age groups separately (see Table 9). Inspecting the model for each of the age groups 33 was important because some studies have indicated that the structure of intellectual abilities changes from childhood to adolescence and to older age (e.g., Lindenberger & Baltes, 1997; Reinert, 1970; Schmidt & Botwinik, 1989). However, consistent with prior findings, the model specified in the present study fitted each of the age groups (cf. Bickley, Keith, & Wolfe, 1995; Kray & Lindenberger, 2000; Schaie, Willis, Jay, & Chipur, 1989; Zelinski & Lewis, 2003), indicating that the five-factor structure seems to be stable across a wide range of ages. 32 Given that the factor knowledge was only represented with one indicator task, it was not included into the model. For the factor inhibitory control, the interference effects in the color and the number version of the Stroop task served as indicators. 33 Although the separate models for each age group are important from a developmental perspective, it should be noted that the sample size per age group in this study (n = 70) was way below the critical limit (N = 100) for structural equation modeling (Bühner, 2004). 148

157 Results Table 9: Summary of the Model-Fitting Procedures Model χ 2 df p value χ 2 /df RMSEA NNFI CFI (Description) M1: Six first-order factors Solution not admissible 34 M2: Five first-order factors model (without inhibitory control) M2: Children M2: Younger adults M2: Older adults Note. CFI = Comparative Fit Index, NNFI = Non-Normed Fit Index, RMSEA = Root Mean Square Error of Approximation. Inspection of the model M2 indicated that the correlations between the latent variables were consistently on a high level. Given that all five domains are assumed to represent abilities from the mechanic component of intelligence (i.e., memory, fluid intelligence, (verbal and perceptual) speed), this finding is not surprising and consistent with previous studies (cf. Duncan et al. 2000; Kray & Lindenberger, 2000; Lindenberger et al., 1993). Also, the correlation between measures with high executive control demands (i.e., WM measures) was higher (.90) than between these executive tasks and the speed measures ( ). However, although high correlations between working memory and fluid intelligence are in line with previous findings (Engle, Tuholski, Laughlin, & Conway, 1999; Fry & Hale, 1996), those between both WM factors (.90) and between visuospatial WM and fluid intelligence (.94) are particularly high. The same pattern was found for each of the age groups, but especially for children and older adults (children:.80 and.96; younger adults.74 and.74; older adults.86 and.87). Given that the WM data are based on span measures (i.e., the number of correctly 34 In case a model completely fails to fit, not all fit indexes are provided, and those indexes provided cannot be interpreted. 149

158 Results recalled trials, see p. 125) and not on the number of correctly solved items within each trial, one may assume that this measurement failed to properly differentiate within children and older adults. This view is supported by the fact that the variability in the performance of the WM tasks was noticeably larger in younger adults than in children, and mostly also larger than in older adults (see Appendix, Table 24), resulting in higher correlations within these age groups. Verbal WM Counting Span Reading Span e7 e Visuospatial WM Symmetry Span Navigation Span e9 e Fluid Intelligence Perceptual Speed Letter Series Figural Reasoning Raven Digit-Symbol Digit-Letter Letter Articulation e11 e12 e13 e14 e15 e16 Verbal Speed Digit Articulation Word Articulation e17 e18 Figure 16: Accepted model M2 with standardized parameter estimates. It should be noted that, especially based on the intercorrelations between the latent variables, the model could of course be further modified. However, given that the primary aim of this analysis was to identify a proper model for pooling the data for the analysis of far 150

159 Results transfer effects (i.e., investigating whether the data of the respective indicator tasks of each construct can be collapsed for this analysis) rather than to examine the structure of the cognitive abilities, further model modifications will be set aside here. Given that the intercorrelations between the indicator tasks were high and M2 yielded very satisfactory fit indices, the analysis of far transfer was based on this structure including five first-order factors (Figure 16) and separate analyses for both versions of the Stroop task and the Spot-a-Word Test. Far Transfer to Other Executive Tasks: The Stroop Task (Prediction 15) Based on the expectation that far transfer to the Stroop task should not - or at least less - be modulated by verbal self-instructions and training variability, the first analysis examined whether the task-switching training groups (2-5) showed a different amount of transfer. Therefore, data for each of the task versions (Color and Number Stroop) were subjected to a four-way ANOVA with the between-subjects factors Age Group (children, younger adults, older adults) and Training Group (switch, verbalization, feedback, variability), as well as the within-subjects factors Session (pretest, posttest) and Trial Type (neutral, incongruent). The mean performance on the level of latencies and accuracy is provided in the Appendix (Table 20 - Table 23). Given that there were no substantial interactions of the factor Training Group with other experimental variables, neither on the level of latencies nor accuracy, data were collapsed across groups 2-5 to increase the statistical power and subjected to a four-way ANOVA with the between-subjects factors Age Group (children, younger adults, older adults) and Training Group (single task, task switching), and the withinsubjects factors Session (pretest, posttest) and Trial Type (neutral, incongruent). Since the CFA showed that interference effects in both task versions were not correlated, the color and the number version were analyzed separately. However, analyses for both task versions showed quadratic age functions with faster latencies in younger adults than in children and 151

160 Results older adults as well as faster performance at posttest than at pretest (all p s <.0001). Also, interference effects were significant in both task versions on the level of latencies and accuracy (all p s <.0001). Thus, these effects will not be reported separately for each task version instead, the focus is on interactions with the factors Age Group and Training Group. Color Stroop Latencies. An interaction between session and age group 35, F(2, 204) = 4.68, p =.01, ŋ 2 =.03, showed that the speeding of RT from pretest to posttest was larger for children than for adults, F(1, 204) = 7.16, p <.01, ŋ 2 =.03, but there was no difference between younger and older adults (p =.14). In addition, this speeding of RT was more pronounced for the taskswitching training group than for the single-task training group, F(1, 204) = 7.92, p <.01, ŋ 2 =.03. Interference effects were larger for adults than for children, and also larger for older adults than for younger adults, F(1, 204) = 7.16, p <.01, ŋ 2 =.02, and F(1, 204) = 7.73, p <.01, ŋ 2 =.02. Importantly, there was an interaction between session, trial type, age group, and training group, F(2, 204) = 4.79, p <.01, ŋ 2 =.03. Post hoc comparisons indicated that children showed a reduction of interference from pretest to posttest, F(1, 68) = 4.31, p <.05, ŋ 2 =.09, but this effect was not modulated by the type of training (p =.17, see Figure 17, upper panel). In contrast, interference in adults interacted with session and training group, F(1, 136) = 7.85, p <.01, ŋ 2 =.05, showing that interference increased from pretest to posttest after single-task training, but that it was reduced after task-switching training (F(1, 26) = 4.45, p <.05, ŋ 2 =.13, and F(1, 110) = 7.86, p <.01, ŋ 2 =.05; see Figure 17, upper panel). Thus, adults showed far transfer to interference control in the Stroop task after task-switching training, but not after singe-task training. Children, however, also showed this far transfer after single-task training. There were no age differences in the amount of transfer after task-switching training (all p s >.15). 35 This interaction failed to reach significance on the level of mean RT (p =.07). 152

161 Results Accuracy. A main effect for age group indicated that children made more errors than adults, F(1, 204) = 10.24, p <.01, ŋ 2 =.04. No further main effects or interactions reached significance. Thus, there was no evidence for far transfer (i.e., a reduction of interference from pretest to posttest) on the level of accuracy. 180 Color Stroop Interference (ms) Single Switch itch Single Switch itch Single Switch itch C hildren Younger Adults O lder A dults Number Stroop Interference (ms) Pretest Posttest Single Switch itch Single Switch itch Single Switch itch C hildren Younger A dults O lder A dults Figure 17: Color Stroop (upper panel) and Number Stroop (lower panel) interference effects (ms) as a function of age group (children, younger adults, older adults), training group (single task, task switching), and session (pretest, posttest). Error bars refer to standard errors of the mean. 153

162 Results Number Stroop Latencies. An interaction between session, age group, and training group, F(2, 204) = 4.09, p <.05, ŋ 2 =.03, showed that the speeding of RT from pretest to posttest in children and younger adults was more pronounced in the task-switching training group than in the singletask training group, whereas the reverse effect was found in older adults. Interference was larger in younger adults than in older adults, F(2, 204) = 5.86, p <.05, ŋ 2 =.01, but there was no difference between children and younger or older adults (p =.54 and.07). An interaction between trial type, age group, and training group, F(2, 204) = 4.17, p <.05, ŋ 2 =.01, pointed to larger interference effects in younger adults than in children and older adults in the taskswitching training group, F(1, 165) = 17.06, p <.0001, ŋ 2 =.38, but there was no difference between children and older adults (p =.76). Importantly, there was a session trial type training group interaction, F(2, 204) = 11.63, p <.001, ŋ 2 =.03, showing that interference effects were generally increased from pretest to posttest after single-task training and reduced after task-switching training, F(1, 39) = 6.00, p <.05, ŋ 2 =.14 36, and F(1, 165) = 10.63, p =.001, ŋ 2 = (see Figure 17, lower panel). However, the interactions with age group failed to reach significance on the level of log-transformed RT (all p s >.06). Thus, results for the number version also showed far transfer of task-switching training to interference control in the Stroop task in all age groups. Accuracy. Children made more response errors than adults, and younger adults also made more errors than older adults, F(1, 204) = 25.08, p <.0001, ŋ 2 =.23, and F(1, 204) = 9.30, p <.01, ŋ 2 =.09. There was a main effect for training group, showing that the single-task training group committed less response errors than the task-switching training group, F(1, 204) = 5.03, p <.05, ŋ 2 =.02. A session age group interaction, F(2, 204) = 7.28, p <.001, ŋ 2 =.06, 36 Analysis based on mean RT yielded a quadratic age effect, F(1, 39) = 4.85, p <.05, ŋ 2 =.09, indicating that this increase was only found in children and older adults, but not in young adults. 37 Based on mean RT, this reduction of interference effects was larger for children than for adults, F(1, 165) = 7.11, p <.01, ŋ 2 =

163 Results revealed that in contrast to adults, children increased their accuracy from pretest to posttest, F(2, 204) = 9.79, p <.01, ŋ 2 =.04. No further main effects and interactions reached significance. However, consistent with the results for the Color Stroop task, there was no evidence for far transfer (i.e., a reduction of interference from pretest to posttest) on the level of accuracy. Although the reduction of Stroop interference from pretest to posttest after taskswitching training was consistent with the initial expectations (see prediction 15), the increase of interference effects after single-task training (in younger and older adults in the color task and in children and older adults in the number task) was somewhat unexpected. To investigate whether this increase in interference costs (i.e., the difference in performance between incongruent and neutral trials) was caused by a more pronounced RT speeding from pretest to posttest in neutral trials or a less pronounced speeding in incongruent trials, separate analyses were performed for neutral and incongruent trials. And indeed, when it comes to younger and older adults, posttest RT for the color task was faster than pretest RT for neutral trials, F(1, 136) = 53.48, p <.0001, ŋ 2 =.29, but less for incongruent trials, F(1, 136) = 39.41, p <.0001, ŋ 2 =.12 (see Table 10). A similar pattern was found for children and older adults in the number task. Thus, the benefit at posttest was more pronounced for neutral trials than for incongruent trials, resulting in larger interference effects for the single-task training group at posttest. Summary. Thus, despite of a few small differences, the general pattern of results for both task versions was consistent: All age groups showed a reduction of interference effects from pretest to posttest after task-switching training, that is, they showed far transfer from task-switching training to the Stroop task (confirming prediction 15). After controlling for differences in baseline performance, there were no age differences in the amount of transfer. In contrast to the initial expectations, children also showed far transfer (i.e., a reduction of interference effects from pretest to posttest) after single-task training in the Color Stroop task. 155

165 Results Far Transfer to Other Executive Tasks: Verbal Working Memory (Predictions 16 and 17) Aside from far transfer of task-switching training to the Stroop task, this study also investigated far transfer to WM abilities. As described in the Method section, two indicator tasks were used to measure verbal WM (Counting Span and Reading Span). Based on the results of the CFA, data were collapsed across both tasks. First, data for the verbal WM abilities (% correct) were subjected to an ANOVA with the between-subjects factor Age Group (children, younger adults, older adults) to examine the pattern of age-related differences at pretest. Results showed a main effect for age group, F(2, 209) = 45.10, p <.0001, ŋ 2 =.30, as well as a quadratic age function, revealing better performance in younger adults than in children and older adults, and also better performance in older adults than in children (all p s <.0001). Thus, age differences in verbal WM abilities at pretest were consistent with prior findings (for reviews, see Hitch, 2006; Park & Payer, 2006), indicating that verbal WM abilities constantly improve during childhood and decline again in older age. Second, far transfer of task-switching training to verbal WM was inspected. The initial hypothesis regarding far transfer of task-switching training was that transfer should be more pronounced in the task-switching training groups (2-5) than in the single-task training group (1). Also, the focus was on whether verbal self-instruction training (groups 3-5) could be transferred to verbal WM tasks (prediction 17). Therefore, the first analysis examined whether the training groups 3-5 (i.e., the groups performing verbal self-instruction during training) showed different amounts of transfer. Data (transfer relative to baseline performance, see p. 125, Method) were subjected to a two-way ANOVA with the between-subjects factor Age Group (children, younger adults, older adults) and Training Group (verbalization, feedback, variability). Since neither the main effects for training group nor the interactions with age group reached significance (all p s >.15), data were collapsed across groups 3-5 to increase statistical power. Mean performances for all experimental conditions are provided in the 157

166 Results Appendix (Table 24). To investigate far transfer of task-switching and verbal self-instruction training, data were then subjected to a two-way ANOVA with the between-subjects factor Age Group (children, younger adults, older adults) and Training Group (single task, task switching, task switching + verbal self-instructions). Results showed a reliable main effect for training group, F(2, 209) = 3.15, p <.05, ŋ 2 =.03. Post-hoc comparisons indicated that transfer was larger after task-switching training than after single-task training, F(1, 209) = 6.17, p =.01, ŋ 2 =.03, but that there was no difference between the task-switching training group (group 2) and the task switching + verbal self-instruction groups (3-5, p =.69) 38 (Figure 18). Neither the main effect for age group nor the age group training group interaction reached significance (p =.11 and.53). Thus, results for verbal WM showed larger performance improvements after task-switching training than after single-task training, that is, there was far transfer of taskswitching training to verbal WM abilities in all age groups (confirming prediction 16). However, there was no far transfer of verbal self-instruction training (disproving prediction 17) Pretest Posttest % correct Single Switch Verbalization Children 0 Single Switch Verbalization Younger Adults 0 Single Switch Verbalization Older Adults Figure 18: Mean performance for verbal WM (% correct) as a function of age group (children, younger adults, older adults), training group (single task, task switching, task switching + verbalization), and session (pretest, posttest). Error bars refer to standard errors of the mean. 38 Note that results did not change when data were not collapsed across groups 3-5, and group 3 was separately compared to groups 1 and 2: Transfer was still larger in group 3 than in group 1, and there was no difference between groups 2 and 3 (p <.05 and p =.89). 158

167 Results Far Transfer to Other Executive Tasks: Visuospatial Working Memory (Prediction 18) In line with the analysis for verbal WM, data for the visuospatial WM abilities (% correct) were subjected to an ANOVA with the between-subjects factor Age Group (children, younger adults, older adults) to examine the pattern of age-related differences at pretest. Results showed a main effect for age group, F(2, 209) = 60.67, p <.0001, ŋ 2 =.37, along with a quadratic age function, indicating better performance in younger adults than in older adults and children, and also better performance in older adults than in children (all p s <.05). Thus, the age differences with respect to visuospatial WM found in this study were generally consistent with previous findings (e.g., DeLuca, 2003; for reviews, see Hitch, 2006; Park & Payer, 2006), indicating that visuospatial WM abilities increase from childhood to adolescence and decline again in older age. For the analysis of far transfer, it was first tested whether the task-switching training groups (groups 2-5) showed different amounts of transfer by subjecting the data (transfer relative to baseline performance, see Method) to a two-way ANOVA with the between-subjects factor Age Group (children, younger adults, older adults) and Training Group (switch, verbalization, feedback, variability). However, neither the main effect for training group nor the interaction with age group reached significance (p =.23 and.93, respectively). Thus, data were collapsed across groups 2-5. Mean performance for all experimental conditions is shown in the Appendix (Table 24). To investigate far transfer, data were then subjected to a two-way ANOVA with the between-subjects factor Age Group (children, younger adults, older adults) and Training Group (single task, task switching). Results showed a reliable main effect for training group, F(1, 204) = 5.34, p <.05, ŋ 2 =.03, indicating that transfer was larger after task-switching 159

168 Results training than after single-task training. Neither the main effect for age group nor the age group training group interaction reached significance (p =.25 and.86) 39. It should be noted that several 40 participants completely failed to answer correctly at pretest. As a result, the difference in performance between pretest and posttest could not be calculated relative to the baseline performance pretest. In the analysis reported above, these missing values were replaced by the respective group means. However, in order to make sure that this procedure did not change the pattern of results, an additional analysis was performed without these subjects. The results were completely consistent with those reported above: The amount of transfer was not different in the task-switching training groups (groups 2-5) (p =.32), but there was more transfer after task-switching training than after single-task training, F(1, 186) = 4.48, p <.05, ŋ 2 =.02. Again, neither the main effect for age group nor the age group training group interaction reached significance (p =.31 and.88) 41. In sum, all age groups showed larger performance improvements from pretest to posttest after task-switching training than after single-task training, that is, there was far transfer of task-switching training to visuospatial WM abilities (confirming prediction 18). 39 Note that the pattern of results did not change when data were not collapsed across groups 2-5. When the single-task training group (group 1) was compared separately to the task-switching training group (group 2), transfer was still larger after task-switching training than after single-task training, F(1, 78) = 7.12, p <.01, ŋ 2 =.08, and neither the main effect for age group nor the age group x training group interaction were significant (p =.33 and.79). 40 Seven children, one younger adult, and ten older adults; four participants per training condition at most. 41 Also, results did not change when data were not collapsed across groups 2-5. A separate comparison of group 1 and 2 still showed more transfer after task-switching training than after single-task training, F(1, 69) = 5.55, p <.05, ŋ 2 =.07, and no effect for age group or age group x training group (p =.42 and.83). 160

169 Results Pretest Posttest % correct Single Children Switch 0 Single Young Adults Switch 0 Single Older Adults Switch Figure 19: Mean performance for spatial WM (% correct) as a function of age group (children, younger adults, older adults), training group (single task, task switching), and session (pretest, posttest). Error bars refer to standard errors of the mean. Far Transfer to Other Another Task Domain: Fluid Intelligence (Prediction 19) In order to examine the pattern of age differences at pretest, data for the fluid intelligence tasks (% correct) were also subjected to an ANOVA with the between-subjects factor Age Group (children, younger adults, older adults). Results showed a main effect for age, F(2, 209) = 64.84, p <.0001, ŋ 2 =.39, with better performance in younger adults than in children and older adults, but the difference between children and older adults failed to reach significance (p =.09). These findings are consistent with prior studies and the two-component model of intelligence (cf. P. B. Baltes et al., 1998; P. B. Baltes et al., 1999). In line with the previous analyses for far transfer, it was first tested whether the taskswitching training groups (groups 2-5) showed different amounts of transfer by subjecting the data (transfer relative to baseline performance) to a two-way ANOVA with the betweensubjects factor Age Group (children, younger adults, older adults) and Training Group (taskswitching, verbalization, feedback, variability). However, neither the main effect for training group nor the interaction with age group reached significance (p =.81 and.82). Thus, data were collapsed across groups 2-5 to increase statistical power. Mean performance for all experimental conditions is shown in the Appendix (Table 24). 161

170 Results In order to investigate far transfer, data were then subjected to a two-way ANOVA with the between-subjects factor Age Group (children, younger adults, older adults) and Training Group (single task, task switching). Results showed a reliable main effect for training group, F(1, 204) = 6.68, p =.01, ŋ 2 =.01, indicating that transfer was larger after task-switching training than after single-task training (see Figure 20). Neither the main effect for age group nor the age group training group interaction reached significance (p =.20 and.58, respectively) 42. Hence, there also was far transfer from task-switching training to another task domain, namely fluid intelligence (confirming prediction 19), but there were no age differences in the amount of transfer. Fluid Intelligence (% correct) Single Children Switch Single Young Adults Switch Single Older Adults Pretest Posttest Switch Figure 20: Mean performance for fluid intelligence (% correct) as a function of age group (children, younger adults, older adults), training group (single task, task switching), and session (pretest, posttest). Error bars refer to standard errors of the mean. 42 The pattern of results did not change when data were not collapsed across groups 2-5. When the single-task training group (group 1) was compared separately to the task-switching training group (group 2), transfer was still larger after task-switching training than after single-task training, F(1, 78) = 5.63, p <.05, ŋ 2 =.07, and neither the main effect for age group nor the age group x training group interaction were significant (p =.53 and.87). 162

171 Results Control Variables (Prediction 20) Finally, this study also included measures for three constructs not supposed to rely on executive control, namely perceptual speed, verbal speed, and knowledge. These measures were included to show that intensive task-switching training does not result in far transfer to these abilities. Therefore, the next set of analyses focused on transfer of task-switching training to the control measures. The mean performance for all experimental conditions is provided in the Appendix (Table 25). To examine age differences at pretest, data for every construct were subjected to an ANOVA with the between-subjects factor Age Group (children, younger adults, older adults); for the analysis of far transfer, data (transfer relative to baseline performance, see Method) were subjected a two-way ANOVA with the additional between-subjects factor Training Group (single, switch, verbalization, feedback, variability). Perceptual Speed Pretest data showed a main effect for age group and a quadratic age function, indicating that younger adults performed better than children and older adults; and older adults also outperformed children (all p s <.0001). However, when it comes to far transfer, neither the main effects for age group and training group nor their interaction reached significance (all p s >.18). Verbal Speed Pretest data showed a main effect for age group and a quadratic age function, revealing that younger adults verbalized faster than children and older adults; and older adults also verbalized faster than children (all p s <.0001). Regarding far transfer, there was a main effect for age group, indicating that the increase in verbal speed from pretest to posttest was smaller for older adults than for younger adults and children (all p s <.05). Nonetheless, 163

172 Results neither the main effect for training group nor the interaction with age group reached significance (all p s >.24). Knowledge Pretest data showed a main effect for age group and a linear age function (see sample description), indicating that younger adults performed better than children, and that older adults outperformed younger adults (all p s <.0001). However, with respect to far transfer, there neither were main effects for age group (p =.07) and training group nor an interaction between them (both p s >.49). All in all, pretest data for the control measures were consistent with the two-component model of intelligence (cf. P. B. Baltes et al., 1998; P. B. Baltes, 1999), but there was no far transfer of task-switching training to tasks not supposed to rely on executive control (confirming prediction 20). 164

173 Results Evaluation of the Training Program This last chapter of the results section is dedicated to the evaluation of the training program. First, the focus is on two different ways for evaluating the effectiveness of the taskswitching training program. The analyses reported above have focused on the comparison of different training conditions, such as single-task and task-switching training, based on mean performance (on the level of mean RT, log-transformed RT, and accuracy) within these training conditions. However, there are at least two other ways to quantify transfer of training and to compare the effectiveness of different types of training (see p. 70). First, the effect sizes for the different training and transfer conditions can be calculated. Second, aside from the group means, the proportion of participants actually showing training and transfer benefits within each training condition can be calculated. In order to consider a given training effective, effect sizes should be at least.30, and more than 50 % of the participants should show training and transfer effects (cf. Derwinger et al., 2003; Klauer, 2001). Therefore, effect sizes (ES) as well as the proportion of participants showing training and transfer benefits are analyzed in this section, structured along the results for near and far transfer reported above 43. Second, the focus was on differential aspects of the training. The question was, which participants benefited most after training and whether individual transfer effects can be predicted. That is, the aim was to examine whether the task-switching training rather results in compensatory effects (i.e, worse performers benefit most) or more in Matthew -effects (i.e., better performers benefit most). 43 These analyses are restricted to the measures yielding transfer effects. Therefore, the control variables are not analyzed. 165

174 Results Inspection of Training and Transfer Effect Sizes In order to examine the effectiveness of the four different task-switching training conditions and the range of transfer across near and far transfer tasks, Cohen s (1977) d, or the standardized mean difference in performance between the beginning and the end of training (for training effects; and between pretest and posttest for transfer effects) was calculated (cf. Verhaeghen et al., 1992). Subsequently, all d-values were corrected for small sample bias using the Hedges and Olkin (1985) correction factor (d ) (for a detailed description, see p. 125, Method). First, ES for the training benefits associated with the four different task-switching training conditions were analyzed. As shown in Figure 21, ES for the task-switching training (group 2) were relatively high in all three age groups (d = ). When the switching training was combined, with the verbal self-instructions, ES increased again in all age groups, particularly for children (d = ). Also in children, ES dropped slightly when feedback (group 4) was provided (d = 1.09), but especially when training tasks were variable (group 5; d =.58). While ES for younger adults were stable across training groups 3-5, older adults showed the largest ES when feedback was provided (d = 1.73), but just as children, a marked decrease when training tasks were variable (d =.17). 166

175 Results Training Effect Size (d') 3,0 2,5 2,0 1,5 1,0 0,5 Children Younger Adults Older Adults 0,0 Switch Verbalization Feedback Variability Training Group Figure 21: Effect size (d ) for the training benefits on the level of specific switch costs as a function of age group (children, younger adults, older adults) and training group (switch, verbalization, feedback, variability). Second, ES for near transfer to a similar switching task were analyzed. Pretestposttest ES for the different training groups are shown in Figure 22. When it comes to general switch costs, there were larger ES for task-switching (d = ) than for single-task training (d = ) in all age groups, but particularly for children. The ES for adults increased again when the switching training was combined with verbalizations (group 3) (d = ). In older adults, they were on a similar level when feedback (group 4) and variability (group 5) came into play (d = ); in younger adults, ES in the feedback condition were similar to the verbalization condition, and maximized in the variability group (d = ). However, the reverse effect was found for children: The verbalizations (d = 1.53), the feedback (d = 1.74), and even more the variable training (d =.70) resulted in substantially smaller ES. Regarding specific switch costs, results for adults were similar: In all age groups, ES were larger after task-switching (d = ) than after single-task training (d = ); for adults, they were on a similar level in the verbalization (d = ) and the feedback (d 167

176 Results = ) condition, and in younger adults maximized in the variability group (d = 1.59). In children, ES were largest in the verbalization (d = 1.96) condition, and reduced when the training was combined with feedback (d =.89) and variability (d =.73). Pretest-Posttest Effect Size d' 2,5 2,0 1,5 1,0 0,5 2,5 2,0 1,5 1,0 0,5 Children Younger Adults Older Adults 0,0 Single Switch Verbalization Feedback Variability General Switch Costs 0,0 Single Switch Verbalization Feedback Variability Specific Switch Costs Figure 22: Effect size (d ) for near transfer benefits on the level of general switch costs (left panel) and specific switch costs (right panel) as a function of age group (children, younger adults, older adults) and training group (single, switch, verbalization, feedback, variability). Another goal of this analysis was to examine the range of far transfer to other executive tasks and other task domains. Effects sizes for far transfer after task-switching and single-task training are shown in Figure 23. Consistent with previous findings (cf. Klauer, 2001), ES were larger for near transfer to a similar switching task than for far transfer to other executive tasks and to another task domain. However, ES after task-switching training were still relatively large even for far transfer, with most values >.70 for children, >.60 for younger adults, and >.40 for older adults, and were quite consistent across the far transfer tasks. In 168

178 Results than one, suggesting that that the task-switching training and even more so the switching training in combination with the verbal self-instructions resulted in substantial training effects, at least when participants were trained with the same tasks in each training session. ES for near as well as far transfer were in line with the previous analyses based on latencies reported above; thus, they further corroborate these results. In addition, they showed that the task-switching training (and for some tasks even the single-task training) completely satisfied Klauer s (2001) criterion for effective trainings (i.e., ES >.30). Proportion of Transfer Another criterion for the evaluation of training and programs is the proportion of participants actually showing training and transfer in each training condition (cf. Derwinger et al., 2003). Again, the first analysis focused on training effects. The proportion of subjects showing training benefits (i.e., a reduction of specific switch costs from the first to the last training session) was calculated for each of the four task-switching training conditions. In the switch group (group 2), 90.5% of participants showed in training-related benefits; so did 88.1% in the verbalization group (group 3), 92.5% in the feedback group (group 4), and 78% in the variability group (group 5). There were no significant differences between the training groups or the age groups (all p s >. 21). Hence, all four training conditions clearly met the requirement of at least 50% participants showing training benefits. Second, the proportion of transferring subjects was calculated for each training condition and each transfer task, so that the task-switching training could be compared with the single-task training 45. Given that there were no age differences (p =.29), data were collapsed across age groups. Results for this analysis are provided in Table 11. And indeed, 45 For each age group and each transfer measure, it was tested whether the four task-switching training groups (group 2-5) were characterized by a different number of transferring subjects. Given that there were no differences between these groups for any of the tasks (all p s >.13), data were collapsed across the task-switching training groups (groups 2-5). 170

179 Results after task-switching training, the proportion of transferring participants was always larger 60 %, and consistent with the findings on the level of effect sizes and latencies, it was also larger for near transfer to a similar switching task (i.e., general and specific switch costs) than for far transfer to other executive tasks and another task domain. Furthermore, the comparison to the single-task training group revealed more transferring subjects for both near and far transfer after task-switching training. Even though this difference failed to reach significance for visuospatial WM and fluid intelligence, results point into the same direction. Table 11: Proportion of Participants showing Near and Far Transfer Effects (%) after Single- Task Training and after Task-Switching Training as a Function of Age Group (Children, Younger Adults, Older Adults) and Transfer Measure Measure Single-task training (%) Task-switching training (%) χ 2 df p General switch costs >.001 Specific switch costs >.001 Color Stroop interference >.05 Number Stroop interference Verbal WM >.05 Visuospatial WM Fluid intelligence Note. n (single-task training) = 42; n = (task-switching training) = 168. Thus, results with respect to the proportion of participants showing training benefits as well as near and far transfer benefits were also consistent with the prior analyses reported above, indicating that the large majority of participants showed training-related improvements 171

180 Results in performance and that the proportion of transfer was always larger after task-switching training than after single-task training. Prediction of Training and Transfer Effects Finally, it also was of interest to examine which participants showed the largest training and transfer benefits and whether these benefits can be predicted. On the one hand, one may assume that subjects initially performing on a relatively low level benefit the most (compensatory effect). On the other hand, it would also be reasonable to expect most benefits in subjects initially performing on a relatively high level ( Matthew -effect, see p. 69). According to Klauer (2001), one way to examine this question is to analyze the status benefit correlations, that is, the correlation between pre-training performance and training benefits as well as between pretest performance and transfer benefits. For compensatory effects, this correlation should be negative, and for Matthew -effects it would be positive. Therefore, in order to inspect training benefits for each of the task-switching training groups, specific switch costs in training session 1 (status) were correlated with the training benefits (i.e., the reduction of specific switch costs from training session 1 to 4). Correlations for all training conditions were substantial (between -.66*** and -.83***, see Table 12) and most importantly, negative. That is, better performance in the beginning of training was associated with less training benefits and vice versa, suggesting that the training had compensatory rather than Matthew -effects in all age groups. Accordingly, in order to assess transfer effects, pretest performance for near and far transfer measures (general switch costs, specific switch costs, Color Stroop interference, Number Stroop interference, verbal WM, visuospatial WM, and fluid intelligence) was correlated with the transfer benefits (i.e., the pretest-posttest difference) for these tasks. Consistent with the analysis of the training effects, substantial negative correlations between status and benefits were found for each of the transfer measures (between -.19* and -.72***, 172

181 Results see Table 13). That is, the poorer participants performed at pretest, the larger were the transfer benefits again, a finding clearly pointing to compensatory effects of the training program 46. However, after simply inspecting the status-benefit correlations for training and transfer effects, a second set of analyses was performed for two reasons: First, although the correlations point to a negative relation between status and benefits, they do not really allow the prediction of training and transfer benefits based on status. Second, no conclusions can be drawn regarding the influence of other experimental variables with respect to the status-benefit relationship (e.g., effects of age). Therefore, in the next set of analyses a hierarchical regression approach was used to further corroborate these findings. The first analysis again focused on the prediction of training benefits. Using the training benefits as the dependent variable, specific switch costs in training session 1 were first entered into the regression procedure. To investigate age differences, age 47 was entered as second independent variable into the regression. This analysis allowed determining whether age added a significant amount of unique variance above that contributed by specific switch costs at the beginning of training. Results of the hierarchical regression analysis are shown in Table 12. And indeed, specific switch costs at the beginning of training (status) turned out to be a reliable predictor for training benefits that is, subjects performing poor at the beginning of training showed the largest training benefits. This relationship was strongest in the groups trained in task-switching and verbal self-instructions by means of the same training tasks (groups 3 and 4), and somewhat smaller in the groups only trained in task switching (group 2) or with variable tasks (group 5), respectively (Figure 24). However, age did not add unique variance for any of the training groups. 46 It should be noted that in this type of analysis, the pretest-error enters the status benefit correlation twice, so that the true correlations may be less negative (Bereiter, 1967). 47 For the regression analysis, the age group variable (children, younger adults, older adults) used in the previous analyses was replaced with the participants exact age. 173

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